Program Details : University Catalogs : University of Minnesota

AGRO 5021 - Plant Breeding Principles

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

This course is intended for advanced undergraduate students and graduate students that are either: 1) not plant breeding majors who will benefit from a basic understanding of how genetics is applied to plant improvement; or 2) plant breeding majors lacking prior coursework in plant breeding. The objective of this course is to develop an understanding of the underlying principles, ideas, and concepts important to applying genetic principles to plant breeding, evaluating breeding methods, and enhancing genetic progress and efficiency.

AGRO 5121 - Applied Experimental Design

Credits:	4.0 [max 4.0]
Course Equivalencies:	Agro 5121/Ent 5121
Typically offered:	Every Spring

Principles of sampling methodologies, experimental design, and statistical analyses. Methods/procedures in generating scientific hypotheses. Organizing, initiating, conducting, and analyzing scientific experiments using experimental designs and statistical procedures. prereq: Stat 5021 or equiv or instr consent

AGRO 5311 - Research Methods in Crop Improvement and Production

Credits:	1.0 [max 1.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall & Summer

Demonstrations and discussions of techniques in crop improvement and/or production research. Presentations integrate biotechnology with traditional breeding methods; production sessions emphasize ecologically sound cropping systems. prereq: applied plant sciences grad

BIOC 5216 - Current Topics in Signal Transduction

Credits:	2.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Spring

Mechanisms by which biological signals evoke biochemical responses.

BIOC 5309 - Biocatalysis and Biodegradation

Credits:	3.0 [max 3.0]
Course Equivalencies:	Bioc 5309/MicE 5309
Typically offered:	Every Spring

Fundamentals of microbial enzymes/metabolism as pertaining to biodegradation of environmental pollutants/biosynthesis for making commodity chemicals. Practical examples. Guest speakers from industry.

BIOC 5351 - Protein Engineering

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Key properties of enzymes/molecular basis, computer modeling strategies, mutagenesis strategies to create protein variants, expression/screening of protein variants. Evaluate research papers, identify unsolved practical/theoretical problems, plan protein engineering experiment.

BIOC 5352 - Biotechnology and Bioengineering for Biochemists

Credits:	3.0 [max 3.0]
Course Equivalencies:	BioC 5352/MicB 5352
Grading Basis:	A-F or Aud
Typically offered:	Periodic Spring

Protein biotechnology. Microorganisms used as hosts for protein expression, protein expression, and engineering methods. Production of enzymes of industrial interest. Applications of protein biotechnology in bioelectronics. Formulation of therapeutic biopharmaceuticals. Recommended prerequisites: Biochemistry (BiOC 3021 or 3022 or 4331) and Microbiology MICB 3301

BIOC 5361 - Microbial Genomics and Bioinformatics

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

Introduction to genomics. Emphasizes microbial genomics. Sequencing methods, sequence analysis, genomics databases, genome mapping, prokaryotic horizontal gene transfer, genomics in biotechnology, intellectual property issues. Hands-on introduction to UNIX shell scripting, genomic data analysis using R and Excel in a computer lab setting. prereq: College-level courses in [organic chemistry, biochemistry, microbiology]

BIOC 5444 - Muscle

Credits:	3.0 [max 3.0]
Course Equivalencies:	BioC 5444/ Phsl 5444
Typically offered:	Every Spring

Muscle molecular structure/function and disease. Muscle regulation, ion transport, and force generation. Muscular dystrophy and heart disease. prereq: 3021 or BIOL 3021 or 4331 or BIOL 4331 or PHSL 3061 or instr consent

BIOC 5528 - Spectroscopy and Kinetics

Credits:	4.0 [max 4.0]
Typically offered:	Every Spring

Biochemical dynamics from perspectives of kinetics and spectroscopy. Influence of structure, molecular interactions, and chemical transformations on biochemical reactions. Focuses on computational, spectroscopic, and physical methods. Steady-state and transient kinetics. Optical and magnetic resonance spectroscopies. prereq: Intro physical chemistry or equiv; intro biochemistry recommended

BIOC 6021 - Biochemistry

Credits:	3.0 [max 3.0]
Course Equivalencies:	BioC 3021/BioC 3022/BioC 4331/
Typically offered:	Every Fall, Spring & Summer

Fundamentals of biochemistry. Structure/function of proteins, nucleic acids, lipids and carbohydrates. Metabolism, regulation of metabolism. Quantitative treatments of chemical equilibria, enzyme catalysis, and bioenergetics. Chemical basis of genetic information flow. prereq: general biology, organic chemistry, instr consent; intended for MBS students

BIOC 8005 - Biochemistry: Structure and Catalysis

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Protein structure, methods to determine structure, protein folding, forces stabilizing macromolecular structure, protein engineering, design. Dynamic properties of proteins/enzymes, enzyme substrate complexes, mechanism of enzyme catalysis.

BIOC 8006 - Biochemistry: Metabolism and Control

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Enzymology of metabolism, metabolic regulation, metabolic control and cell signaling.

BIOC 8007 - Molecular Biology of the Genome

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

This course explores the molecular biology of the eukaryotic genome and transcriptome, focusing on fundamental genetic processes, molecular mechanisms, and their relationships to biology and disease. Students gain a firm understanding of the key concepts and techniques through lectures, reading, and discussions. Students learn to critically analyze scientific papers through student-led presentations and discussions. They gain experience in articulating scientific questions, formulating testable hypotheses, and designing experiments. This course promotes development of science writing skills.

BIOC 8008 - Molecular Biology of the Transcriptome

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

This course explores the molecular biology of the eukaryotic genome and transcriptome, focusing on fundamental genetic processes, molecular mechanisms, and their relationships to biology and disease. Students gain a firm understanding of the key concepts and techniques through lectures, reading, and discussions. Students learn to critically analyze scientific papers through student-led presentations and discussions. They gain experience in articulating scientific questions, formulating testable hypotheses, and designing experiments. This course promotes development of science writing skills.

BIOC 8102 - Hot Topics in the Biology of Aging

Credits:	1.0 [max 1.0]
Typically offered:	Spring Odd Year

This course is intended to provide a platform of understanding about the major issues surrounding biological research in aging. This course will include a combination of student- and faculty-led discussions on select research topics that are highly relevant to the field of biogerontology research, along with instruction/discussions on scientific integrity. Student participants will lead discussions focused on their area of research expertise, utilizing a combination of review articles and research articles. Discussion of scientific misconduct will include case studies. This course is open to graduate students and post-doctoral fellows involved in the National Institutes on Aging (NIA) training grant ?Functional Proteomics of Aging?. This course is also open to other graduate students or post-doctoral fellows who are conducting biological research in aging with instructor?s permission.

BIOL 4003 - Genetics

Credits:	3.0 [max 3.0]
Course Equivalencies:	Biol 4003/GCD 3022
Typically offered:	Every Fall, Spring & Summer

Genetic information, its transmission from parents to offspring, its expression in cells/organisms, and its course in populations. prereq: Biol 2003/2003H or BioC 3021 or BioC 4331 or grad

BIOL 5950 - Special Topics

Credits:	1.0 -4.0 [max 8.0]
Typically offered:	Periodic Fall, Spring & Summer

In-depth study of special topic in life sciences.

BMEN 5111 - Biomedical Ultrasound

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Introduction to biomedical ultrasound, including physics of ultrasound, transducer technology, medical ultrasound imaging, photoacoustic imaging, applications of non-linear acoustics, and high-intensity ultrasound. prereq: [[PHYS 1302 or equiv], [MATH 2374 or equiv]] or instr consent

BMEN 5201 - Advanced Biomechanics

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Introduction to biomechanics of musculoskeletal system. Anatomy, tissue material properties. Kinematics, dynamics, and control of joint/limb movement. Analysis of forces/motions within joints. Application to injury, disease. Treatment of specific joints, design of orthopedic devices/implants. prereq: [[3001 or equiv], [CSE upper div or grad student]] or instr consent

BMEN 5311 - Advanced Biomedical Transport Processes

Credits:	3.0 [max 3.0]
Course Equivalencies:	BMEn 5311/ChEn 5753/ME 5381
Typically offered:	Every Spring

Fluid flow and mass transfer in the body, bioreactors, and medical devices. Pulsatile flows. Flows around curved and deformable vessels. Boundary layer flows. Blood rheology. Interstitial (porous media) flows. Oxygenation. Cell migration. Student critiques of published papers.

BMEN 5321 - Microfluidics in Biology and Medicine

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Fundamentals of microfluidics. Fluid mechanics/transport phenomena in microscale systems. Pressure/surface driven flows. Capillary forces, electrokinetics, hydraulic circuit analysis. Finite element modeling for microfluidic systems. Design/fabrication methods for microfluidic devices. prereq: [3111, AEM 4201, ChEn 4005, [ME 3331 or ME 3332 or CSE grad student or instr consent]

BMEN 5351 - Cell Engineering

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Engineering approaches to cell-related phenomena important to cell/tissue engineering. Receptor/ligand binding. Trafficking/signaling processes. Applications to cell proliferation, adhesion, and motility. Cell-matrix interactions. prereq: [2401, [2501 or concurrent registration is required (or allowed) in 5501], [MATH 2243 or MATH 2373]] or CSE upper div or grad student or instr consent

BMEN 5411 - Neural Engineering

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Theoretical basis. Signal processing techniques. Modeling of nervous system, its response to stimulation. Electrode design, neural modeling, cochlear implants, deep brain stimulation. Prosthetic limbs, micturition control, prosthetic vision. Brain machine interface, seizure prediction, optical imaging of nervous system, place cell recordings in hippocampus. prereq: 3401 recommended

BMEN 5412 - Neuromodulation

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Fundamentals of bioengineering approaches to modulate the nervous system, including bioelectricity, biomagnetism, and optogenetics. Computational modeling, design, and physiological mechanisms of neuromodulation technologies. Clinical exposure to managing neurological disorders with neuromodulation technology.

BMEN 5413 - Neural Decoding and Interfacing

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Neural interface technologies currently in use in patients as well as the biophysical, neural coding, and hardware features relating to their implementation in humans. Practical and ethical considerations for implanting these devices into humans. prereq: CSE upper division student, CSE graduate student, or instructor approval. recommended: BMEn 3411

BMEN 5501 - Biology for Biomedical Engineers

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Concepts of cell/tissue structure/function. Basic principles of cell biology. Tissue engineering, artificial organs. prereq: Engineering upper div or grad student

BMEN 5701 - Cancer Bioengineering

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Cancer-specific cell, molecular/genetics events. Quantitative applications of bioinformatics/systems biology, optical imaging, cell/matrix mechanics. Drug transport (with some examination of design of novel therapeutics). prereq: [Upper division CSE undergraduate, CSE graduate student] or instr consent

BMEN 8101 - Biomedical Digital Signal Processing

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Signal processing theory for analyzing real world digital signals. Digital signal processing and mathematically derived algorithms for analysis of stochastic signals. Spectral analyses, noise cancellation, optimal filtering, blind source separation, beamforming techniques. prereq: [[MATH 2243 or MATH 2373], [MATH 2263 or MATH 2374]] or equiv

BMEN 8431 - Controlled Drug and Gene Delivery: Materials, Mechanisms, and Models

Credits:	4.0 [max 4.0]
Course Equivalencies:	BMEn 8431/PHM 8431
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Physical, chemical, physiological, mathematical principles underlying design of delivery systems for drugs. Small molecules, proteins, genes. Temporal controlled release. prereq: Differential equations course including partial differential equations or instr consent

CHEM 5755 - X-Ray Crystallography

Credits:	4.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Essentials of crystallography as applied to modern, single crystal X-ray diffraction methods. Practical training in use of instrumentation in X-ray crystallography facility in Department of Chemistry. Date collection, correction/refinement, structure solutions, generation of publication materials, use of Cambridge Crystallographic Structure Database. prereq: Chem grad student or instr consent

CHEM 8011 - Mechanisms of Chemical Reactions

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Reaction mechanisms and methods of study. Mechanistic concepts in chemistry. Gas phase reactions to mechanisms, "electron pushing" mechanisms in organic reactions, mechanism of enzymatic reactions. Kinetic schemes and other strategies to investigate mechanisms. prereq: 2302 or equiv

CHEM 8021 - Computational Chemistry

Credits:	4.0 [max 4.0]
Typically offered:	Every Spring

Modern theoretical methods used in study of molecular structure, bonding, reactivity. Concepts/practical applications. Determination of spectra, relationship to experimental techniques. Molecular mechanics. Critical assessment of reliability of methods. prereq: 4502 or equiv

CHEM 8152 - Analytical Spectroscopy

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Survey of analytical spectroscopic methods. Design/application of spectroscopic instruments, including signal generation, acquisition, and interpretation. May include nuclear magnetic resonance, electron paramagnetic resonance, infrared and ultraviolet/visible spectroscopy, and mass spectrometry. prereq: grad chem major or instr consent

CHEM 8157 - Bioanalytical Chemistry

Credits:	4.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Periodic Spring

Theory and practical aspects of analytical methods used in determination/characterization of biologically important materials. Enzymatic/kinetic methods in study of proteins, carbohydrates, lipids, and nucleic acids.

CHEM 8411 - Introduction to Chemical Biology

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Chemistry of amino acids, peptides, proteins, lipids, carbohydrates, and nucleic acids. Structure, nomenclature, synthesis, and reactivity. Overview of techniques used to characterize these biomolecules. prereq: 2302 or equiv

CHEM 8412 - Chemical Biology of Enzymes

Credits:	4.0 [max 4.0]
Course Equivalencies:	Chem 8412/MedC 8412
Typically offered:	Periodic Spring

Enzyme classification with representative examples from current literature. Strategies used to decipher enzyme mechanisms. Chemical approaches for control of enzyme catalysis. prereq: 2302 or equiv

CHEM 8413 - Nucleic Acids

Credits:	4.0 [max 4.0]
Course Equivalencies:	Chem 8413/MedC 8413
Typically offered:	Periodic Fall

Chemistry and biology of nucleic acids: structure, thermodynamics, reactivity, DNA repair, chemical oligonucleotide synthesis, antisense approaches, ribozymes, overview of techniques used in nucleic acid research, interactions with small molecules and proteins. prereq: 2302 or equiv

CHEM 8541 - Dynamics

Credits:	4.0 [max 4.0]
Course Equivalencies:	Chem 5541/8541
Typically offered:	Periodic Fall

Mathematical methods for physical chemistry. Classical mechanics/dynamics, normal modes of vibration. Special topics such as rotational motion, Langevin equation, Brownian motion, time correlation functions, collision theory, cross sections, energy transfer, molecular forces, potential energy surfaces, classical electrostatics, Shannon entropy. prereq: Undergrad physical chem course

CHEM 8551 - Quantum Mechanics I

Credits:	4.0 [max 4.0]
Course Equivalencies:	Chem 5551/8551
Typically offered:	Every Fall

Review of classical mechanics. Postulates of quantum mechanics with applications to determination of single particle bound state energies and scattering cross-sections in central field potentials. Density operator formalism with applications to description of two level systems, two particle systems, entanglement, and Bell inequality. prereq: undergrad physical chem course

CHEM 8552 - Quantum Mechanics II

Credits:	2.0 [max 4.0]
Typically offered:	Every Spring

Second Quantization;Density matrices; Molecular Electronic Structure Theory; Hartree-Fock Theory; Electron Correlation; Configuration Interaction; Perturbation Theory; Energy Derivatives; Coupled-Cluster;Density Functional Theory; Relativistic Quantum Chemistry; prereq: 8551

CHEM 8561 - Thermodynamics, Statistical Mechanics, and Reaction Dynamics I

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Two-part sequence. Thermodynamics, equilibrium statistical mechanics, ensemble theory, partition functions. Applications, including ideal gases/crystals. Theories of simple liquids, Monte Carlo, and molecular dynamics simulations. Reaction dynamics from microscopic viewpoint. prereq: undergrad physical chem course

CHEM 8565 - Chemical Reaction Dynamics

Credits:	2.0 [max 2.0]
Typically offered:	Periodic Spring

Fundamentals of chemical reaction dynamics including potential energy surfaces, collision theory, statistical mechanical background and transition state theory, variational transition state theory, activation energy, tunneling, unimolecular reactions, energy transfer, reactions in solution, solvation free energy, potential of mean force, quasithermodynamic treatment, reactions in solution, diffusion control, Kramers� theory, and photochemistry

CMB 8208 - Neuropsychopharmacology

Credits:	3.0 [max 3.0]
Course Equivalencies:	CMB 8208/ NSc 8208/Phcl 8208/P
Grading Basis:	A-F or Aud
Typically offered:	Fall Even Year

Relationships between drugs. Biochemical, behavioral, neurophysiological consequences. Functional biogenic amine, peptidergic, other pathways. Neuronal function/behavior. Feedback mechanisms, induction, inhibition. Stimulants, hallucinogens, depressants, opiates. Student presentations. prereq: graduate student and instr consent

ECP 5620 - Drug Metabolism and Disposition

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Spring Odd Year

Oxidatative/conjugative enzymes systems involved in human drug metabolism/disposition. Various in vitro models used to evaluate drug metabolism or chemical entity, pros/cons of each. Factors involved in conducting in vivo studies. Components used to predict in vivo drug disposition from in vivo studies. prereq: Grad student or instr consent

ECP 8500 - Advances in Pharmacometrics Modeling and Simulation

Credits:	1.0 [max 6.0]
Grading Basis:	S-N only
Typically offered:	Every Fall & Spring

Modeling/simulation at interface between physiological/pharmacological processes. Current literature, discussion groups. Computer applications using relevant software programs. prereq: Grad student in ECP or PHM or instr consent

ECP 8503 - Intermediate Population PK/PD Methods

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Periodic Fall & Spring

This course will present the theory and hands-on application of intermediate population methods using nonlinear mixed-effects model applied to pharmacologic systems.

EEB 5042 - Quantitative Genetics

Credits:	3.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Fall

Fundamentals of quantitative genetics. Genetic/environmental influences on expression of quantitative traits. Approaches to characterizing genetic basis of trait variation. Processes that lead to change in quantitative traits. Applied/evolutionary aspects of quantitative genetic variation. prereq: [BIOL 4003 or GCD 3022] or instr consent; a course in statistics is recommended

GCD 4151 - Molecular Biology of Cancer

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Regulatory pathways involved in directing normal development of complex eukaryotic organisms, how disruptions of these pathways can lead to abnormal cell growth/cancer. Causes, detection, treatment, prevention of cancer. prereq: Biol 4003

GCD 5036 - Molecular Cell Biology

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Analysis of dynamic cellular activities at the molecular level in cell biological fields that are experiencing new research advances not yet reflected in textbooks. Significant emphasis is placed on understanding the experimental basis of our current knowledge of cellular processes through analysis of scientific papers. Project and presentation-based assessments of learning outcomes. prereq: BIOL 4004 or GCD 4005W or grad

GCD 8008 - Mammalian Gene Transfer and Genome Engineering

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Current gene transfer and genome engineering technology. Applications of genetic modifications in animals, particularly transgenic animals and human gene therapy. prereq: instr consent

GCD 8073 - Genetics & Genomics in Human Health

Credits:	2.0 [max 3.0]
Typically offered:	Every Spring

Application of molecular, biochemical, chromosomal, and population genetics to human variation and disease. Abnormal chromosome number and structure; abnormal enzyme, structural protein, receptor, and transport; analysis of inheritance patterns; behavioral genetics; genetic basis of common disease. Current research articles in human genetics. prereq: 8131 or BIOL 4003 or instr consent

GCD 8103 - Human Histology

Credits:	5.0 [max 5.0]
Course Equivalencies:	GCD 6103/8103
Typically offered:	Every Fall

Light/electron microscopic anatomy of tissues and their organization into human organs. Emphasizes integrating structure, its relationship to function at levels from molecules to organs. Lecture, lab. prereq: Undergraduate biology, chemistry, math, and physics course; instr consent

GCD 8131 - Advanced Molecular Genetics and Genomics

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

Literature-based course in modern molecular genetic and genomic analysis. Students will gain a deep understanding of the fundamental molecular mechanisms controlling inheritance in biological systems. Students will gain a facility in thinking critically and creatively about how genes work at cellular, organismal, and transgenerational levels. Course instruction emphasizes active-learning approaches, student presentations, and group projects. prereq: [3022 or BIOL 4003], [BIOC 3021 or BIOC 4331] or instr consent

GCD 8151 - Cellular Biochemistry and Cell Biology

Credits:	2.0 -4.0 [max 4.0]
Grading Basis:	A-F only
Typically offered:	Every Fall

This course introduces graduate students to fundamental concepts of Biochemical Unity (Part 1) and Cell Theory (Part 2). For Part 1, we will discuss matter of life, equilibrium, entropy & law of mass action, two state systems, random walks & diffusion, rate equations of chemical reactions, and explore how they relate to regulation of biological networks (gene regulation and signal transduction). For Part 2 we will focus on properties of biological membranes, membrane trafficking, protein import & degradation, nuclear structures and their function, as well as molecular motors, cytoskeletal dynamics, and mitosis. The course assumes students have had previous undergraduate courses in cell biology, biochemistry and genetics. prereq: [[[4034 or 8121 or BioC 8002], Biol 4004] or BMBB or MCDBG grad student] or instr consent

GCD 8161 - Advanced Cell Biology and Development

Credits:	2.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Spring

The advanced cell and developmental biology of embryos, taught through in-depth, comparative analysis of historical and current primary research articles that illustrate developmental mechanisms and experimental approaches in key invertebrate and vertebrate model organisms. prereq:[BMBB or MCDBG grad student] or [GCD 4161, [GCD 8131 or Biol 4003], Biol 4004, and GCD 4034] or instr consent

GCD 8920 - Special Topics

Credits:	1.0 -4.0 [max 4.0]
Typically offered:	Every Fall & Spring

Special topic shell

HORT 8280 - Current Topics in Applied Plant Sciences

Credits:	1.0 [max 1.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall & Spring

Topics presented by faculty or visiting scientists. prereq: Grad major in [hort or applied plnt sciences or ent or agro or plnt brdg or plnt path or soil] or instr consent

MEDC 8001 - General Principles of Medicinal Chemistry

Credits:	3.0 [max 3.0]
Course Equivalencies:	MedC 5700/MedC 8001
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Fundamental principles of molecular recognition, physiochemical properties of drugs, drug metabolism and disposition, interaction of molecules with DNA/RNA. prereq: Med chem grad student or instr consent

MEDC 8002 - General Principles of Medicinal Chemistry

Credits:	3.0 [max 3.0]
Course Equivalencies:	MedC 5710/MedC 8002
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Fundamental principles of molecular recognition, physicochemical properties of drugs, drug metabolism and disposition, interaction of molecules with DNA/RNA. prereq: Med chem grad student or instr consent

MEDC 8413 - Chemistry of Nucleic Acids

Credits:	4.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Spring Even Year

Chemical aspects of nucleic acid structure and function, synthesis, and functional variants. prereq: [Medicinal chem or chem or biochem] grad student

MEDC 8435 - BioAssay & Data Analysis

Credits:	1.0 [max 1.0]
Prerequisites:	MEDC 8001 or instructor permission.
Grading Basis:	A-F or Aud
Typically offered:	Spring Even Year

Emphasis is an intro to bioassay & rodent experimental design approaches, data analysis & basic statistical analysis of corresponding data. Concepts of what instrumentation resources are available within the Department of Medicinal Chemistry & the Institute for Therapeutics Discovery & Development (ITDD), what the corresponding bioassays that can be measured on those resources, considerations & criteria for the development of a new bioassay, how to design basic rodent (mouse & rat) animal experiments including power-analysis (how to predict the number of animals needed for the experiment), as well as data analysis [mean, standard error of the mean (SEM), standard deviation of the mean (SD)] & statistical analysis [student t-test, one-way Anova, two-way Anova, & appropriate post-hoc tests). prereq: MEDC 8001 or instructor permission.

MEDC 8753 - MOLECULAR TARGETS OF DRUG DISCOVERY

Credits:	3.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Fall Even Year

Structure of biological macromolecules that are targets of drugs. Techniques to accelerate directed drug discovery. Protein structure/interactions. Popular target classes. Computational tools for visualizing/analyzing protein-ligand and protein-protein interactions. Structural characterization at a level sufficient to underpin critical data evaluation. Biophysical techniques to assess weak ligand binding and suitable for fragment-based lead discovery. prereq: 5710 or 8002 or CHEM 5412 or structural biochemistry or instr consent

MICA 8002 - Structure, Function, and Genetics of Bacteria and Viruses

Credits:	4.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Structure, function, and metabolism of microorganisms. Microbial genetics. Molecular virology. prereq: [One undergrad or grad course each in [microbiology, genetics, biochemistry]] or instr consent

MICA 8003 - Immunity and Immunopathology

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Lymphocyte activation, signal transduction in lymphocytes, antigen receptor genetics, antigen presentation, lymphoid anatomy, adaptive immune responses to microbes, immunodeficiency, immunopathology, cytokines, transplantation, autoimmunity. prereq: Upper level undergrad immunology course or instr consent

MICA 8004 - Cellular and Cancer Biology

Credits:	4.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Fundamental concepts in cellular, molecular, and genetic basis of disease. Molecular basis of inflammation and cancer metastasis. Genetic basis for inherited disorders and gene therapy. Molecular mechanisms of pathogenesis. prereq: [One undergrad or grad course each in [biochemistry, cell biology]] or instr consent

MICA 8005 - Topics in Microbiology, Immunology, and Cancer Biology

Credits:	1.0 -4.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall & Spring

Colloquium format. Readings/discussion on specialized topic. prereq: 8012, [8002 or 8003 or 8004] or instr consent

MICA 8009 - Biochemical Aspects of Normal and Abnormal Cell Growth and Cell Death

Credits:	2.0 [max 2.0]
Typically offered:	Every Spring

Aspects of mechanisms involved in growth control at level of nuclear function. Neoplasia in hormonal cancers (such as prostate cancer) and role of protein phosphorylation in normal and abnormal growth. Mechanisms of cell death via apoptosis and its implications in normal and abnormal proliferation. prereq: 8004 or [BioC 3021, Biol 4004] or instr consent

MICA 8011 - Current Topics in Immunology

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Colloquium format. In-depth reading, discussion prereq: MICA 8003 or instr consent

MICA 8013 - Translational Cancer Research

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Clinical issues in cancer research. Discuss translational research projects as they pertain to a variety of cancers. prereq: 8004 or instr consent

MICA 8094 - Research in Microbiology, Immunology, and Cancer Biology

Credits:	1.0 [max 5.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall, Spring & Summer

One-on-one research training from faculty adviser during laboratory rotation. prereq: 1st yr MICa grad student

MICE 5035 - Personal Microbiome Analysis

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Personal Microbiome Analysis, an introduction to the computational exploration and analysis of your inner microbial community, also known as your microbiome. In this course, you will have the opportunity to explore your own microbiome using visualization and analysis tools. Sequencing your own microbiome is encouraged but not required for the course. Introductory biology or genetics is recommended: BIOL 1009, GCD 3022 or BIOL 4003.

NSC 5461 - Cellular and Molecular Neuroscience

Credits:	3.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Lectures by team of faculty, problem sets in important physiological concepts, discussion of original research papers. prereq: NSc grad student or instr consent

NSC 5561 - Systems Neuroscience

Credits:	4.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Principles of organization of neural systems forming the basis for sensation/movement. Sensory-motor/neural-endocrine integration. Relationships between structure and function in nervous system. Team taught. Lecture, laboratory. prereq: NSc grad student or instr consent

NSC 5661 - Behavioral Neuroscience

Credits:	2.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Neural coding/representation of movement parameters. Neural mechanisms underlying higher order processes such as memorization, memory scanning, and mental rotation. Emphasizes experimental psychological studies in human subjects, single cell recording experiments in subhuman primates, and artificial neural network modeling. prereq: Grad NSc major or grad NSc minor or instr consent

NSC 8026 - Neuro-Immune Interactions

Credits:	3.0 [max 3.0]
Course Equivalencies:	MVB 8361/NSc 8026/Psy 8026
Typically offered:	Periodic Fall & Spring

Regulatory systems (neuroendocrine, cytokine, and autonomic nervous systems) linking brain and immune systems in brain-immune axis. Functional effects of bidirectional brain-immune regulation. Course is offered fall of even-numbered years. prereq: 5561, MicB 4131

NSC 8111 - Quantitative Neuroscience

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Principles of experimental design and statistical analysis in neuroscience research. Includes an introduction to computer programming for data analysis using both classic and modern quantitative methods.

NSC 8211 - Developmental Neurobiology

Credits:	2.0 -4.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

How neuronal types develop. Emphasizes general mechanisms. Experimental data demonstrating mechanisms. prereq: Neuroscience grad student or instr consent

NSC 8481 - Advanced Neuropharmaceutics

Credits:	4.0 [max 4.0]
Course Equivalencies:	CMB 8481/NSc 8481/Phm 8481
Grading Basis:	A-F or Aud
Typically offered:	Fall Even Year

Delivery of compounds to central nervous system (CNS) to activate proteins in specific brain regions for therapeutic benefit. Pharmaceutical/pharmacological issues specific to direct drug delivery to CNS. prereq: instr consent

PHAR 5201 - Applied Medical Terminology

Credits:	2.0 [max 2.0]
Course Equivalencies:	Phar 1002/Phar 5201
Typically offered:	Every Fall, Spring & Summer

Interested in learning the difference between an antigen and an antibiotic? During this course, you will not only increase your medical vocabulary by more than 2500 words at your own pace, you will also learn to identify and articulately describe a wide variety of medical conditions and processes. Communication related to disease states, procedures, and diagnostics in health care can sometimes seem like another language. This course will help you recognize medical abbreviations, relate terms to procedures and diagnostics, and comprehend the meaning of medical terminology by using word elements. If you are interested in the health care field or would like to understand more about your own medical care, this course is a great place to start. Prereq: Basic knowledge of human anatomy/physiology

PHAR 5700 - Applied Fundamentals of Pharmacotherapy

Credits:	3.0 [max 3.0]
Course Equivalencies:	Phar 3700/Phar 5700
Grading Basis:	A-F only
Typically offered:	Every Fall & Spring

Pharmacotherapy, the treatment of disease through the administration of medications, is a field particularly interesting to many health care workers. This course is designed to introduce students to some of the main drug classes available for the treatment of particular diseases. Students will also learn about basic pharmacology, recognize brand and generic drug names, and explore their common uses and therapeutic classes. A basic understanding of treatment options available for common disease states will also be developed during this course. Additionally, the course develops basic proficiency in the use of drug information resources. This is a completely online course with due dates throughout the semester, though students have the option to work ahead if they choose. This course is offered each Fall, Spring, and Summer term. For more information, contact phar3700@umn.edu or 612-624-7976. Prereq: Medical terminology recommended

PHCL 5109 - Introduction to Scientific Communication

Credits:	1.0 -18.0 [max 18.0]
Typically offered:	Every Fall, Spring & Summer

This course is an interactive classroom experience focused on developing student communication skills. The primary emphasis is on student presentations of their research projects. In addition to making verbal presentations, students are expected to provide constructive criticism and feedback to their peers. Students also work on scientific writing skills by preparing a one-page NIH-style Specific Aims page outlining their research project. Prerequisites: student in the Graduate Program in Pharmacology (MS program) or approval from the Director of Graduate Studies Keywords: Pharmacology, Directed, Independent Study, Biomedical, Basic Science, Research, Drug

PHCL 5110 - Introduction to Pharmacology

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

This is a course for first-year students in the Graduate Program in Pharmacology. The course introduces students to the basic principles of pharmacology and focuses on molecular mechanisms of drug action. Topics covered include pharmacokinetics, pharmacodynamics, signal transduction, toxicology pharmacogenomics, and drug discovery. Prerequisites: student in the Graduate Program in Pharmacology or approval from the Course Director(s) Keywords: Introduction, Pharmacology, Molecular, Drug, Pharmacokinetics, Pharmacodynamics, Protein, Pharmacokinetics

PHCL 5111 - Pharmacogenomics

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Human genetic variation, its implications. Functional genomics, pharmacogenomics, toxicogenomics, proteomics. Interactive, discussion-based course. prereq: Grad student or instr consent Keywords: Pharmacology, Pharmacogenomics, Toxicogenomics, Proteomics, Genetics, Drug

PHCL 5462 - Neuroscience Principles of Drug Abuse

Credits:	2.0 [max 2.0]
Course Equivalencies:	Phcl 5462/Nsc 5462
Typically offered:	Periodic Spring

Current research on drugs of abuse, their mechanisms of action, characteristics shared by various agents, and neural systems affected by them. Offered biennially, spring semester of even-numbered years. prereq: instr consent

PHCL 8014 - Small RNA Biology

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Small RNAs as major regulators of gene/protein expression. MicroRNAs and their potential use in diagnosis/prognosis of various disease conditions, including cancers. Biology of small RNAs and their role in health and disease. prereq: BIOC 8002 or MICA 8004 or equiv or instr consent

PHCL 8026 - Neuro-Immune Interactions

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Regulatory systems (neuroendocrine, cytokine, autonomic nervous systems) linking brain/immune systems in brain-immune axis. Functional effects of bidirectional brain-immune regulation. prereq: MICA 8001 or equiv or instr consent

PHSL 5061 - Principles of Physiology for Biomedical Engineering

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Human physiology with emphasis on quantitative aspects. Organ systems (circulation, respiration, renal, gastrointestinal, endocrine, muscle, central and peripheral nervous systems), cellular transport processes, and scaling in biology. prereq: Biomedical engineering grad, one yr college chem and physics and math through integral calculus

PHSL 5096 - Integrative Biology and Physiology Research Advances

Credits:	1.0 [max 4.0]
Grading Basis:	A-F only
Typically offered:	Every Fall & Spring

Attend/participate in IBP Fall/Spring seminar series. Seminars given by faculty, invited speakers, students. Exposure to key topics. How to present seminars. prereq: instr consent

PHSL 5101 - Human Physiology

Credits:	5.0 [max 5.0]
Course Equivalencies:	INMD 6814/PHSL 5101
Typically offered:	Every Spring

Survey of human physiology: Cardiovascular, muscle, respiratory, gastrointestinal, nutrition, renal physiology. Integrative, systems approach. Emphasizes normal function. prereq: Grad student

PHSL 5197 - Stress Physiology

Credits:	1.0 -3.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Spring

Journal club format. Meets weekly to examine foundations of stress, historical progress, development of stress, modern stress physiology. Focus on stress-induced pathology with attention to cardiovascular, metabolic, neuroendocrine disorders. Students participating in the weekly discussion are assessed on discussion participation, completion of weekly writing assignments and quality of the presentation in the class, are eligible for 1 credit. Students completing a midterm (test) and a final project (specific aims page of an NIH RO1 grant) in addition to the criteria described above are eligible for 3 credits. Prerequisite: instructor consent is required. Graduate student standing, master students, and post-doctoral fellows (if they are eligible for credits). Undergraduate students must have taken PHSL 3061 or equivalent, and have previous laboratory research experience.

PHSL 5211 - Physiology of Inflammation in Disease

Credits:	3.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Spring

In this course, we will explore the latest developments in the field of inflammation-mediated chronic diseases. The students will learn basic concepts of immunity and inflammation and the mechanisms by which non-infectious inflammatory processes mediate chronic disease. Instructor consent is required. Courses in physiology, such as PHSL3051, 3061, and Microbiology and Immunology, such as MICB 4131, are recommended but not required.

PHSL 5444 - Muscle

Credits:	3.0 [max 3.0]
Course Equivalencies:	BioC 5444/ Phsl 5444
Typically offered:	Every Spring

Muscle molecular structure/function and disease. Muscle regulation, ion transport, and force generation. Muscular dystrophy and heart disease. prereq: PHSL 3061 or PHSL 5061 or BioC 3021, BIOL 3021 or BIOL 4331 or instr consent

PHSL 5510 - Advanced Cardiac Physiology and Anatomy

Credits:	2.0 -3.0 [max 3.0]
Typically offered:	Every Spring

Fundamental concepts, advanced topics related to clinical/biomedical cardiac physiology. Lectures, laboratories, workshops, anatomical dissections. Intense, one week course. prereq: instr consent

PHSL 5525 - Anatomy and Physiology of the Pelvis and Urinary System

Credits:	1.0 -2.0 [max 2.0]
Course Equivalencies:	Anat 5525/Phsl 5525
Grading Basis:	A-F only
Typically offered:	Every Spring

Two-day intensive course. Pelvis, perineum, and urinary system with cadaveric dissection. Structure/function of pelvic and urinary organs, including common dysfunction and pathophysiology. Laboratory dissections, including kidneys, ureters, urinary bladder, pelvic viscera and perineum (male or female), pelvic floor, vascular and nervous structures. Grand rounds section. prereq: One undergrad anatomy course, one undergrad physiology course, instr consent

PHSL 6051 - Systems Physiology

Credits:	4.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring & Summer

General physiology, endocrine, circulatory, respiratory, digestive, energy metabolism, and renal physiology examined at molecular, cellular, and organ level. Emphasizes homeostasis and basic regulatory aspects of physiological processes of organ systems. prereq: [Prev or current] neuroscience course; [biochemistry, human anatomy] recommended

PLPA 5480 - Principles of Plant Pathology

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

This course is intended for graduate students and undergraduate students in their third or fourth year that are interested in learning about principles of plant pathology, diseases that affect plants, microbiology and microbial and plant interactions. In this course students will learn principles of plant pathology through lectures and demonstrations and exercises in laboratory. Students will gain knowledge of mycology and select diseases caused by fungi within Ascomycota, Basidiomycota and the fungal-like Oomycota. Diseases caused by bacteria, nematodes, viruses, parasitic plants and abiotic damage are also examined. Lectures will include information concerning the history and importance of plant pathology, mycology, bacteriology, nematology, virology, infection process, genetics of host and microorganism interactions, epidemiology of diseases and disease control strategies. In the hands-on laboratory period the student will learn laboratory skills, gain experience using the microscope, work with microorganisms, learn diagnostic skills, and be able to recognize 30 plant diseases. prereq: BIOL 1009 or equiv

PLPA 8104 - Plant Virology

Credits:	2.0 [max 2.0]
Grading Basis:	A-F only
Typically offered:	Every Spring

Characteristics, biology, epidemiology, and control of plant diseases caused by viruses. prereq: 5480

PSY 8026 - Neuro-Immune Interactions

Credits:	3.0 [max 3.0]
Course Equivalencies:	MVB 8361/NSc 8026/Psy 8026
Typically offered:	Periodic Fall

Regulatory systems (neuroendocrine, cytokine, and autonomic nervous systems) linking brain and immune systems in brain-immune axis. Functional effects of bidirectional brain-immune regulation. prereq: MicB 4131 or equiv, NSc 5111 or equiv

PUBH 6159 - Principles of Toxicology I

Credits:	2.0 [max 2.0]
Grading Basis:	A-F only
Typically offered:	Every Fall

This is the first of two courses that covers fundamental principles of exposure, uptake and metabolism. This course focuses on identifying the mechanisms and effects of chemical, biological, and physical agents on human health. Discussions will focus on the action of environmental agents and how they interact with humans to cause disease. Emphasis is on understanding the principles of toxicology as they apply to understanding toxicant-human interactions.

PUBH 6160 - Principles of Toxicology II

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

This second part of the Principles of Toxicology course is focused on toxicodynamics. In this course,students will learn to apply their knowledge of basic toxicokinetic principles and metabolic systems to elucidate mechanisms of toxicity induced by xenobiotic compounds. In addition, they will learn basic principles of omics-based approaches and methodologies, and how such data can be integrated to assess and predict adverse effects of chemical exposures across multiple levels of biological complexity. At the end of the course, students will give a scientific presentation on a published article of their choice (approved by instructors) that explores the mechanism of a toxicodynamic process. prereqs: Biochemistry and PubH 6104 or permission of the instructor

PUBH 6182 - Emerging Infectious Disease: Current Issues, Policies, and Controversies

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Issues/controversies surrounding emerging infectious diseases. Framework for considering realistic/innovative policies. Bioterrorism, public health preparedness. Pandemic influenza preparedness, smallpox vaccination, antibiotic resistance. prereq: AHC student, instr consent

PUBH 6320 - Fundamentals of Epidemiology

Credits:	3.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Fall, Spring & Summer

This course provides an understanding of basic methods and tools used by epidemiologists to study the health of populations.

PUBH 6381 - Genetics in Public Health in the Age of Precision Medicine

Credits:	2.0 [max 2.0]
Typically offered:	Every Fall

Our understanding of human genomic variation and its relationship to health is expanding rapidly. This knowledge is now being translated primarily through the field of ?precision medicine? (finding the right drug for the right person at the right time). Public health, in contrast, seeks to abate the social and environmental factors that lead to disease and health disparities. This course will provide an introduction to the field of public health genomics at this interesting point in its history. Approximately one-half of the course is devoted to Genetic Epidemiology, or the science of detecting genetic risk factors for human disease. The other half of the course will cover public health genomics, including ?precision public health?, genetic screening programs, and the possibilities and pitfalls of direct to consumer marketing of genetic tests. How genomics relates to health equity will be a recurring theme of this course. This is a graduate course designed primarily for Epidemiology MPH and PhD students, and fulfills the ?Epi Of? requirement for the MPH in Epidemiology. Graduate students from other programs are very welcome.

PUBH 7415 - Introduction to Clinical Trials

Credits:	3.0 [max 3.0]
Course Equivalencies:	PubH 3415/PubH 7415
Typically offered:	Every Fall & Summer

Hypotheses/endpoints, choice of intervention/control, ethical considerations, blinding/randomization, data collection/monitoring, sample size, analysis, writing. Protocol development, group discussions. prereq: 6414 or 6450 or one semester graduate-level introductory biostatistics or statistics or instr consent

PUBH 7470 - Study Designs in Biomedical Research

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Diagnostic medicine, including methods for ROC curve. Bioassays. Early-phase clinical trials, methods including dose escalation, toxicity, and monitoring. Quality of life. prereq: [[6450, 6451] or equiv], [grad student in biostatistics or statistics or clinical research], familiarity with SAS

PUBH 8160 - Advanced Toxicology

Credits:	2.0 [max 2.0]
Grading Basis:	A-F only
Typically offered:	Every Fall

Cellular/molecular mechanisms by which xenobiotics cause toxicity. Investigative approaches to current research problems in toxicology/carcinogenesis. Apoptosis, cell cycle regulation, genetic toxicology, molecular mechanisms of chemical carcinogenesis, genetic basis for susceptibility to environmental toxicants. prereq: 6160, one course in biochem, one course in molecular biol, instr consent

SCB 8181 - Stem Cell Biology

Credits:	3.0 [max 3.0]
Course Equivalencies:	GCD 8181/SCB 8181
Typically offered:	Every Fall

Stem cell research and its applications. Critical analysis, written summaries/critiques, oral presentations. prereq: [[GCD 4034], [GCD 4161]] or equiv or instr consent

VMED 5165 - Surveillance of Foodborne Diseases and Food Safety Hazards

Credits:	2.0 [max 2.0]
Course Equivalencies:	PubH 5181/VMed 5165
Typically offered:	Every Spring

Principles/methods for surveillance of foodborne diseases. Investigation of outbreaks. Assessment of food safety hazards. Focuses on integration of epidemiologic/lab methods. prereq: [PUBH 5330, [professional school or grad student]] or instr consent

VMED 5180 - Ecology of Infectious Disease

Credits:	3.0 [max 3.0]
Course Equivalencies:	CMB 5180/PubH 6180/PubH 6380
Typically offered:	Every Fall

How host, agent, environmental interactions influence transmission of infectious agents. Environmental dissemination, eradication/control, evolution of virulence. Use of analytical/molecular tools.

VMED 5240 - Advanced Small Animal Pathobiology I

Credits:	1.0 [max 1.0]
Grading Basis:	A-F only
Typically offered:	Fall Even Year

Biology, physiology, pathophysiology, and medicine of disciplines relevant to companion animals. Pathogenesis/treatment of diseases. Developing hypotheses that can be translated into clinical research. Prereq CVM grad student, [DVM or foreign equiv] degree.

VMED 5243 - Advanced Small Animal Pathobiology IV

Credits:	1.0 [max 1.0]
Grading Basis:	A-F only
Typically offered:	Spring Odd Year

Overview of biology, physiology, pathophysiology, and medicine. Underlying pathogenesis/treatment of diseases of companion animals. Developing hypotheses that could be translated into clinical research. Prereq CVM grad student, [DVM or foreign equiv] degree.

BIOL 5272 - Applied Biostatistics

Credits:	4.0 [max 3.0]
Course Equivalencies:	Biol 3272Biol 3272H//Biol 5272
Grading Basis:	A-F only
Typically offered:	Every Fall & Spring

Conceptual basis of statistical analysis. Statistical analysis of biological data. Data visualization, descriptive statistics, significance tests, experimental design, linear model, simple/multiple regression, general linear model. Lectures, computer lab. prereq: High school algebra; BIOL 2003 recommended.

CSCI 5304 - Computational Aspects of Matrix Theory

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Perturbation theory for linear systems and eigenvalue problems. Direct/iterative solution of large linear systems. Matrix factorizations. Computation of eigenvalues/eigenvectors. Singular value decomposition. LAPACK/other software packages. Introduction to sparse matrix methods. prereq: 2031 or 2033 or instr consent

EE 8231 - Optimization Theory

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

Introduction to optimization in engineering; approximation theory. Least squares estimation, optimal control theory, and computational approaches. prereq: instr consent

IE 8521 - Optimization

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Theory and applications of linear and nonlinear optimization. Linear optimization: simplex method, convex analysis, interior point method, duality theory. Nonlinear optimization: interior point methods and first-order methods, convergence and complexity analysis. Applications in engineering, economics, and business problems. prereq: Familiarity with linear algebra and calculus.

LING 5801 - Introduction to Computational Linguistics

Credits:	3.0 [max 3.0]
Typically offered:	Spring Odd Year

Methods/issues in computer understanding of natural language. Programming languages, their linguistic applications. Lab projects. prereq: [4201 or 5201] or programming experience or instr consent

MATH 5385 - Introduction to Computational Algebraic Geometry

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Geometry of curves/surfaces defined by polynomial equations. Emphasizes concrete computations with polynomials using computer packages, interplay between algebra and geometry. Abstract algebra presented as needed. prereq: [2263 or 2374 or 2573], [2243 or 2373 or 2574]

MATH 5445 - Mathematical Analysis of Biological Networks

Credits:	4.0 [max 4.0]
Typically offered:	Every Spring

Development/analysis of models for complex biological networks. Examples taken from signal transduction networks, metabolic networks, gene control networks, and ecological networks. prereq: Linear algebra, differential equations

MATH 5467 - Introduction to the Mathematics of Image and Data Analysis

Credits:	4.0 [max 4.0]
Typically offered:	Every Spring

Background theory/experience in wavelets. Inner product spaces, operator theory, Fourier transforms applied to Gabor transforms, multi-scale analysis, discrete wavelets, self-similarity. Computing techniques. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283 or instr consent]; [[2263 or 2374], 4567] recommended

MATH 5485 - Introduction to Numerical Methods I

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Solution of nonlinear equations in one variable. Interpolation, polynomial approximation. Methods for solving linear systems, eigenvalue problems, systems of nonlinear equations. prereq: [2243 or 2373 or 2573], familiarity with some programming language

MATH 5525 - Introduction to Ordinary Differential Equations

Credits:	4.0 [max 4.0]
Typically offered:	Periodic Fall & Spring

Ordinary differential equations, solution of linear systems, qualitative/numerical methods for nonlinear systems. Linear algebra background, fundamental matrix solutions, variation of parameters, existence/uniqueness theorems, phase space. Rest points, their stability. Periodic orbits, Poincare-Bendixson theory, strange attractors. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283]

MATH 5535 - Dynamical Systems and Chaos

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall & Spring

Dynamical systems theory. Emphasizes iteration of one-dimensional mappings. Fixed points, periodic points, stability, bifurcations, symbolic dynamics, chaos, fractals, Julia/Mandelbrot sets. prereq: [2243 or 2373 or 2573], [2263 or 2374 or 2574]

MATH 5583 - Complex Analysis

Credits:	4.0 [max 4.0]
Course Equivalencies:	Math 3574/Math 5583
Typically offered:	Every Fall, Spring & Summer

Algebra, geometry of complex numbers. Linear fractional transformations. Conformal mappings. Holomorphic functions. Theorems of Abel/Cauchy, power series. Schwarz' lemma. Complex exponential, trig functions. Entire functions, theorems of Liouville/Morera. Reflection principle. Singularities, Laurent series. Residues. prereq: 2 sems soph math [including [2263 or 2374 or 2573], [2283 or 3283]] recommended

MATH 5587 - Elementary Partial Differential Equations I

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Emphasizes partial differential equations w/physical applications, including heat, wave, Laplace's equations. Interpretations of boundary conditions. Characteristics, Fourier series, transforms, Green's functions, images, computational methods. Applications include wave propagation, diffusions, electrostatics, shocks. prereq: [2243 or 2373 or 2573], [2263 or 2374 or 2574]

MATH 5588 - Elementary Partial Differential Equations II

Credits:	4.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Heat, wave, Laplace's equations in higher dimensions. Green's functions, Fourier series, transforms. Asymptotic methods, boundary layer theory, bifurcation theory for linear/nonlinear PDEs. Variational methods. Free boundary problems. Additional topics as time permits. prereq: [[2243 or 2373 or 2573], [2263 or 2374 or 2574], 5587] or instr consent

MATH 5651 - Basic Theory of Probability and Statistics

Credits:	4.0 [max 4.0]
Course Equivalencies:	Math 5651/Stat 5101
Typically offered:	Every Fall & Spring

Logical development of probability, basic issues in statistics. Probability spaces, random variables, their distributions/expected values. Law of large numbers, central limit theorem, generating functions, sampling, sufficiency, estimation. prereq: [2263 or 2374 or 2573], [2243 or 2373]; [2283 or 2574 or 3283] recommended.

MATH 5652 - Introduction to Stochastic Processes

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall & Spring

Random walks, Markov chains, branching processes, martingales, queuing theory, Brownian motion. prereq: 5651 or Stat 5101

MATH 5707 - Graph Theory and Non-enumerative Combinatorics

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall & Spring

Basic topics in graph theory: connectedness, Eulerian/Hamiltonian properties, trees, colorings, planar graphs, matchings, flows in networks. Optional topics include graph algorithms, Latin squares, block designs, Ramsey theory. prereq: [2243 or 2373 or 2573], [2263 or 2374 or 2574]; [2283 or 3283 or experience in writing proofs] highly recommended; Credit will not be granted if credit has been received for: 4707

MATH 5711 - Linear Programming and Combinatorial Optimization

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall & Spring

Simplex method, connections to geometry, duality theory, sensitivity analysis. Applications to cutting stock, allocation of resources, scheduling problems. Flows, matching/transportation problems, spanning trees, distance in graphs, integer programs, branch/bound, cutting planes, heuristics. Applications to traveling salesman, knapsack problems. prereq: 2 sems soph math [including 2243 or 2373 or 2573]

MATH 8401 - Mathematical Modeling and Methods of Applied Mathematics

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Dimension analysis, similarity solutions, linearization, stability theory, well-posedness, and characterization of type. Fourier series and integrals, wavelets, Green's functions, weak solutions and distributions. prereq: 4xxx numerical analysis and applied linear algebra or instr consent

MATH 8441 - Numerical Analysis and Scientific Computing

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Approximation of functions, numerical integration. Numerical methods for elliptic partial differential equations, including finite element methods, finite difference methods, and spectral methods. Grid generation. prereq: [4xxx analysis, 4xxx applied linear algebra] or instr consent

MATH 8445 - Numerical Analysis of Differential Equations

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Finite element and finite difference methods for elliptic boundary value problems (e.g., Laplace's equation) and solution of resulting linear systems by direct and iterative methods. prereq: 4xxx numerical analysis, 4xxx partial differential equations or instr consent

MATH 8583 - Theory of Partial Differential Equations

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Classification of partial differential equations/characteristics. Laplace, wave, heat equations. Some mixed problems. prereq: [Some 5xxx PDE, 8601] or instr consent

MATH 8584 - Theory of Partial Differential Equations

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Fundamental solutions/distributions, Sobolev spaces, regularity. Advanced elliptic theory (Schauder estimates, Garding's inequality). Hyperbolic systems. prereq: 8583 or instr consent

MSBA 6331 - Big Data Analytics

Credits:	3.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Fall

Exploring big data infrastructure and ecosystem, ingesting and managing big data, analytics with big data; Hadoop, MapReduce, Hive, Spark, scalable machine Learning, scalable real-time streaming analytics, NoSQL, cloud computing, and other recent developments in big data.

MSBA 6451 - Optimization and Simulation for Decision Making

Credits:	3.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Spring

Fundamentals of decision analysis, linear optimization, mixed integer linear programming, Bayesian inference, Monte Carlo simulation, and decision technologies.

PUBH 6310 - Clinical Epidemiology 1

Credits:	1.0 [max 1.0]
Typically offered:	Every Spring

Clinical epidemiology is the science of using population methods to answer individual patient questions. In this course in clinical epidemiology, I will cover the design of epidemiological studies and the analysis and interpretation of epidemiological data in order to answer clinical questions. A variety of study design methods including cohort, case-control, and cross-sectional study designs will be taught. The design and analysis of clinical trials are covered in-depth by other courses (e.g. PUBH 7420 and 7415) and hence are not covered here. This course is intended for MS students majoring in clinical research. If you are in the clinical research certificate program and have an MD, you can enroll in this course. If you are in the clinical research certificate program and do NOT have an MD, please contact the instructor for permission prior to enrolling. Others including those in MPH programs in the School of Public Health and other interested students should contact the instructor for permission prior to enrolling. If you have already studied advanced methods in epidemiology or biostatistics or completed Epi Methods II (PUBH 6342) or more advanced Epidemiology courses, please do not take this 1-credit course since there will be redundant material. You may be interested instead in Clinical Epidemiology II, which focuses on more clinical aspects including prognosis, diagnosis, treatment, and prevention.

PUBH 6311 - Clinical Epidemiology II

Credits:	1.0 [max 1.0]
Typically offered:	Every Spring

Clinical epidemiology is the science of using population methods to answer individual patient questions. This course in clinical epidemiology will cover the design of epidemiological studies and the analysis and interpretation of epidemiological data in order to answer clinical questions. Clinical Epidemiology II will cover concepts related to prognosis, diagnosis, treatment, and prevention. This course is intended for MS students majoring in clinical research. If you are in the clinical research certificate program and have an MD, please contact the instructor for permission. Students in PhD programs in the School of Public Health are welcome to enroll as long as they meet the course requirements. This course is not suitable for MPH students. COURSE PREREQUISITES ? Fundamentals of Epidemiology (PUBH 6320; grade of B- or higher) OR Epidemiological Methods I (PUBH 6341; grade B- or higher), or equivalent. ? Clinical Epidemiology I (PUBH 6310; grade B- or higher) OR Epidemiological Methods II (PUBH 6342; grade B- or higher), or equivalent. ? Biostatistics Literacy (PUBH 6414; grade of B- or higher) OR Biostatistics I (PUBH 6450; grade B- or higher), or equivalent.

PUBH 6341 - Epidemiologic Methods I

Credits:	3.0 [max 3.0]
Course Equivalencies:	PubH 6320PubH /6341
Grading Basis:	A-F only
Typically offered:	Every Fall

Introduction to epidemiologic concepts and methods: (1) Study design (randomized trials and observational studies); (2) Measures of exposure-disease association; (3) Casual inference and bias; (4) Confounding and effect modification.

PUBH 6342 - Epidemiologic Methods II

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Methods and techniques for designing, implementing, analyzing, and interpreting observational epidemiologic studies, including cohort, case-control, and cross-sectional studies.

PUBH 6343 - Epidemiologic Methods III

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Analysis/interpretation of data from various epidemiological study designs. SAS used to demonstrate epidemiological/statistical concepts in data analysis. prereq: [6342, 6451] with a grade of at least B- or instr consent

PUBH 6366 - Modeling and Mapping for Infectious Disease Epidemiology

Credits:	2.0 [max 2.0]
Typically offered:	Every Fall

Infectious disease epidemiology is a topic within the field of epidemiology that covers: 1) Principles and concepts of infectious disease transmission dynamics necessary to understand how and why diseases spread, and 2) Epidemiologic methods, including study designs, needed to quantify key aspects of an infectious disease This course will discuss: 1) How to use modeling to gain insight into the spread and control of infectious disease, and 2) The role that geography and GIS plays in gaining insights into the emergence and spread of an infectious disease. Students will learn key epidemiologic concepts that determine who is at risk for acquiring an infectious disease, how infectious diseases spread, and what measures can be taken to prevent or control the spread of an infectious disease. This course will focus on how simulation modeling and spatial analyses can provide insights into what contributes to the spread of an infectious disease. In addition, students will learn how to read and critically review peer-reviewed publications on infectious disease epidemiology using examples drawn from local, national, and international settings.

PUBH 6385 - Epidemiology and Control of Infectious Diseases

Credits:	2.0 [max 2.0]
Typically offered:	Every Spring

Principles and/ methods. Strategies for disease control and prevention, including immunization. Relevance of modes of transmission of specific agents for disease spread and prevention. Public health consequences of infectious diseases at local, national, and international levels.

PUBH 6386 - Cardiovascular Disease Epidemiology and Prevention

Credits:	2.0 [max 2.0]
Typically offered:	Every Spring

The course will provide an introduction to cardiovascular disease (CVD) epidemiology. It is intended to provide a detailed perspective on the well-established risk factors for CVD, as well as an introduction to emerging risk factors. Both observational studies and clinical trials will be discussed. The class will include a main focus on prevention of cardiovascular disease, and national recommendations for treatment and prevention. Several classes will incorporate discussions of new directions and current controversies in CVD. Additionally, the class will introduce students to the CVD research in the Division of Epidemiology and Community Health.

PUBH 6387 - Cancer Epidemiology

Credits:	2.0 [max 2.0]
Typically offered:	Spring Odd Year

Epidemiologic aspects of cancer. Theories of carcinogenesis, patterns of incidence and mortality, site-specific risk factors. Issues of cancer control and prevention.

PUBH 6414 - Biostatistical Literacy

Credits:	3.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Fall, Spring & Summer

Develop ability to read/interpret statistical results in primary literature. Minimal calculation. No formal training in any statistical programming software. Biostatistical Literacy will cover the fundamental concepts of study design, descriptive statistics, hypothesis testing, confidence intervals, odds ratios, relative risks, adjusted models in multiple linear, logistic and Poisson regression, and survival analysis. The focus will be when to use a given method and how to interpret the results, not the actual computation or computer programming to obtain results from raw data. prereq: MPH or certificate student or environmental health or instr consent

PUBH 6450 - Biostatistics I

Credits:	4.0 [max 4.0]
Grading Basis:	A-F only
Typically offered:	Every Fall & Spring

This course will cover the fundamental concepts of exploratory data analysis and statistical inference for univariate and bivariate data, including: ? study design and sampling methods, ? descriptive and graphical summaries, ? random variables and their distributions, ? interval estimation, ? hypothesis testing, ? relevant nonparametric methods, ? simple regression/correlation, and ? introduction to multiple regression. There will be a focus on analyzing data using statistical programming software and on communicating the results in short reports. Health science examples from the research literature will be used throughout the course. prereq: [College-level algebra, health sciences grad student] or instr consent

PUBH 6451 - Biostatistics II

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall & Spring

This course will cover more advanced aspects of statistical analysis methods with a focus on statistical modeling, including: ? two-way ANOVA, ? multiple linear regression, ? logistic regression, ? Poisson regression, ? log binomial and ordinal regression, ? survival analysis methods, including Kaplan-Meier analysis and proportional hazards (Cox) regression, ? power and sample size, and ? survey sampling and analysis. There will be a focus on analyzing data using statistical programming software and on communicating the results in short reports. Health science examples from the research literature will be used throughout the course. prereq: [PubH 6450 with grade of at least B, health sciences grad student] or instr consent

PUBH 6541 - Statistics for Health Management Decision Making

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Variation. Frequency distribution, measurement, probability, graphing. Significance tests, estimation, trends; data handling. Modeling, odds ratios. Prevalence, incidence and vital statistics. Research applications. Statistical approach to rational administrative decision making. Inductive teaching, lectures, computer/lab exercises. prereq: Health care admin student or instr consent

PUBH 7401 - Fundamentals of Biostatistical Inference

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Part of two-course sequence intended for PhD students in School of Public Health who need rigorous approach to probability/statistics/statistical inference with applications to research in public health. prereq: Background in calculus; intended for PhD students in public hlth and other hlth sci who need rigorous approach to probability/statistics and statistical inference with applications to research in public hlth

PUBH 7402 - Biostatistics Modeling and Methods

Credits:	4.0 [max 4.0]
Typically offered:	Every Spring

Second of two-course sequence. Rigorous approach to probability/statistics, statistical inference. Applications to research in public health. prereq: 7401; intended for PhD students in health sciences

PUBH 7405 - Biostatistical Inference I

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

T-tests, confidence intervals, power, type I/II errors. Exploratory data analysis. Simple linear regression, regression in matrix notation, multiple regression, diagnostics. Ordinary least squares, violations, generalized least squares, nonlinear least squares regression. Introduction to General linear Model. SAS and S-Plus used. prereq: [[Stat 5101 or concurrent registration is required (or allowed) in Stat 5101], biostatistics major] or instr consent

PUBH 7406 - Biostatistical Inference II

Credits:	3.0 [max 4.0]
Typically offered:	Every Spring

This course introduces students to a variety of concepts, tools, and techniques that are relevant to the rigorous design and analysis of complex biomedical studies. Topics include ANOVA, sample-size calculations, multiple testing, missing data, prediction, diagnostic testing, smoothing, variable selection, the bootstrap, and nonparametric tests. R software will be used. Biostatistics students are strongly encouraged to typeset their work using LaTeX or in R markdown. prereq: [7405, [STAT 5102 or concurrent registration is required (or allowed) in STAT 5102], biostatistics major] or instr consent

PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Introduction to and methodology of randomized clinical trials. Design issues, sample size, operational details, interim monitoring, data analysis issues, overviews. prereq: 6451 or concurrent registration is required (or allowed) in 6451 or 7406 or instr consent

PUBH 7430 - Statistical Methods for Correlated Data

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Correlated data arise in many situations, particularly when observations are made over time and space or on individuals who share certain underlying characteristics. This course covers techniques for exploring and describing correlated data, along with statistical methods for estimating population parameters (mostly means) from these data. The focus will be primarily on generalized linear models (both with and without random effects) for normally and non-normally distributed data. Wherever possible, techniques will be illustrated using real-world examples. Computing will be done using R and SAS. prereq: Regression at the level of PubH 6451 or PubH 7405 or Stat 5302. Familiarity with basic matrix notation and operations (multiplication, inverse, transpose). Working knowledge of SAS or R (PubH 6420).

PUBH 7440 - Introduction to Bayesian Analysis

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Introduction to Bayesian methods. Comparison with traditional frequentist methods. Emphasizes data analysis via modern computing methods: Gibbs sampler, WinBUGS software package. prereq: [[7401 or STAT 5101 or equiv], [public health MPH or biostatistics or statistics] grad student] or instr consent

PUBH 7445 - Statistics for Human Genetics and Molecular Biology

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Introduction to statistical problems arising in molecular biology. Problems in physical mapping (radiation hybrid mapping, DDP), genetic mapping (pedigree analysis, lod scores, TDT), biopolymer sequence analysis (alignment, motif recognition), and micro array analysis. prereq: [6450, [6451 or equiv]] or instr consent; background in molecular biology recommended

PUBH 8401 - Linear Models

Credits:	3.0 [max 4.0]
Typically offered:	Every Fall

This course is concerned with the theory and application of linear models. The first part of the course will focus on general linear model theory from a coordinate-free geometric approach. The second half of the course covers theory, applications and computing for linear models, and concentrates on modeling, computation and data analysis. It is intended as a core course for biostatistics PhD students and statistics PhD students. prereq: [[7405, concurrent registration is required (or allowed) in STAT 8101] or instr consent], calculus, familiar wtih matrix/linear algebra

PUBH 8432 - Probability Models for Biostatistics

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Three basic models used for stochastic processes in the biomedical sciences: point processes (emphasizes Poisson processes), Markov processes (emphasizes Markov chains), and Brownian motion. Probability structure and statistical inference studied for each process. prereq: [7450, 7407, Stat 5102, [advanced biostatstics or statistics] major] or instr consent

PUBH 8442 - Bayesian Decision Theory and Data Analysis

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Theory/application of Bayesian methods. Bayesian methods compared with traditional, frequentist methods. prereq: [[7460 or experience with FORTRAN or with [C, S+]], Stat 5101, Stat 5102, Stat 8311, grad student in [biostatistics or statistics]] or instr consent

PUBH 8472 - Spatial Biostatistics

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Spatial data, spatial statistical models, and spatial inference on unknown parameters or unobserved spatial data. Nature of spatial data. Special analysis tools that help to analyze such data. Theory/applications. prereq: [[STAT 5101, STAT 5102] or [STAT 8101, STAT 8102]], some experience with S-plus; STAT 8311 recommended

PUBH 8475 - Statistical Learning and Data Mining

Credits:	3.0 [max 3.0]
Course Equivalencies:	PubH 7475/PubH 8475/Stat 8056
Typically offered:	Periodic Spring

Statistical techniques for extracting useful information from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles (such as bagging/boosting), unsupervised learning. prereq: [[[6450, 6451, 6452] or STAT 5303 or equiv], [biostatistics or statistics PhD student]] or instr consent

STAT 5021 - Statistical Analysis

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall & Spring

Intensive introduction to statistical methods for graduate students needing statistics as a research technique. prereq: college algebra or instr consent; credit will not be granted if credit has been received for STAT 3011

STAT 5052 - Statistical and Machine Learning

Credits:	3.0 [max 4.0]
Grading Basis:	A-F only
Typically offered:	Every Fall

The material covered will be the foundations of modern machine learning methods including regularization methods, discriminant analysis, neural nets, random forest, bagging, boosting, support vector machine, and clustering. Model comparison using cross-validation and bootstrap methods will be emphasized.

STAT 5101 - Theory of Statistics I

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Logical development of probability, basic issues in statistics. Probability spaces. Random variables, their distributions and expected values. Law of large numbers, central limit theorem, generating functions, multivariate normal distribution. prereq: (MATH 2263 or MATH 2374 or MATH 2573H), (MATH 2142 or CSCI 2033 or MATH 2373 or MATH 2243)

STAT 5102 - Theory of Statistics II

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall & Spring

Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory. prereq: [5101 or Math 5651 or instr consent]

STAT 5201 - Sampling Methodology in Finite Populations

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Simple random, systematic, stratified, unequal probability sampling. Ratio, model based estimation. Single stage, multistage, adaptive cluster sampling. Spatial sampling. prereq: 3022 or 3032 or 3301 or 4102 or 5021 or 5102 or instr consent

STAT 5302 - Applied Regression Analysis

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall, Spring & Summer

Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications. prereq: 3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions.

STAT 5303 - Designing Experiments

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall, Spring & Summer

Analysis of variance. Multiple comparisons. Variance-stabilizing transformations. Contrasts. Construction/analysis of complete/incomplete block designs. Fractional factorial designs. Confounding split plots. Response surface design. prereq: 3022 or 3032 or 3301 or 4102 or 5021 or 5102 or instr consent

STAT 5401 - Applied Multivariate Methods

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.

STAT 5421 - Analysis of Categorical Data

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

Varieties of categorical data, cross-classifications, contingency tables. Tests for independence. Combining 2x2 tables. Multidimensional tables/loglinear models. Maximum-likelihood estimation. Tests for goodness of fit. Logistic regression. Generalized linear/multinomial-response models. prereq: STAT 3022 or 3032 or 3301 or 5302 or 4051 or 8051 or 5102 or 4102

STAT 5511 - Time Series Analysis

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models. prereq: STAT 4102 or STAT 5102

STAT 5601 - Nonparametric Methods

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

Order statistics. Classical rank-based procedures (e.g., Wilcoxon, Kruskal-Wallis). Goodness of fit. Topics may include smoothing, bootstrap, and generalized linear models. prereq: Stat classes 3032 or 3022 or 4102 or 5021 or 5102 or instr consent

STAT 5701 - Statistical Computing

Credits:	3.0 [max 3.0]
Prerequisites:	(Stat 5102 or Stat 8102) and (Stat 5302 or STAT 8051) or consent
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Statistical programming, function writing, graphics using high-level statistical computing languages. Data management, parallel computing, version control, simulation studies, power calculations. Using optimization to fit statistical models. Monte Carlo methods, reproducible research. prereq: (Stat 5102 or Stat 8102) and (Stat 5302 or STAT 8051) or consent

STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Linear/generalized linear models, modern regression methods including nonparametric regression, generalized additive models, splines/basis function methods, regularization, bootstrap/other resampling-based inference. prereq: Statistics grad or instr consent

STAT 8052 - Applied Statistical Methods 2: Design of Experiments and Mixed -Effects Modeling

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Design experiments/analyze data with fixed effects, random/mixed effects models. ANOVA for factorial designs. Contrasts, multiple comparisons, power/sample size, confounding, fractional factorials. Computer-generated designs. Response surfaces. Multi-level models. Generalized estimating equations (GEE) for longitudinal data with non-normal errors. prereq: 8051 or instr consent

STAT 8053 - Applied Statistical Methods 3: Multivariate Analysis and Advanced Regression

Credits:	3.0 [max 3.0]
Prerequisites:	PhD student in stat or DGS permission and 8052
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Standard multivariate analysis. Multivariate linear model, classification, clustering, principal components, factor analysis, canonical correlation. Topics in advanced regression. prereq: PhD student in stat or DGS permission and 8052

STAT 8054 - Statistical Methods 4: Advanced Statistical Computing

Credits:	3.0 [max 3.0]
Prerequisites:	STAT 8053 or #
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Optimization, numerical integration, Markov chain Monte Carlo, related topics. prereq: STAT 8053 or instr consent

STAT 8056 - Statistical Learning and Data Mining

Credits:	3.0 [max 3.0]
Grading Basis:	OPT No Aud
Typically offered:	Periodic Spring

STAT8056 covers a range of emerging topics in machine learning and data science, including high-dimensional analysis, recommender systems, undirected and directed graphical models, feed-forward networks, and unstructured data analysis. This course will introduce various statistical and computational techniques for prediction and inference. These techniques are directly applicable to many fields, such as business, engineering, and bioinformatics. This course requires the basic knowledge of machine learning and data mining (e.g., STAT8053).

STAT 8101 - Theory of Statistics 1

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Review of linear algebra. Introduction to probability theory. Random variables, their transformations/expectations. Standard distributions, including multivariate Normal distribution. Probability inequalities. Convergence concepts, including laws of large numbers, Central Limit Theorem. delta method. Sampling distributions. prereq: Statistics grad major or instr consent

STAT 8102 - Theory of Statistics 2

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Statistical inference. Sufficiency. Likelihood-based methods. Point estimation. Confidence intervals. Neyman Pearson hypothesis testing theory. Introduction to theory of linear models. prereq: 8101, Statistics graduate major or instr consent

STAT 8111 - Mathematical Statistics I

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Probability theory, basic inequalities, characteristic functions, and exchangeability. Multivariate normal distribution. Exponential family. Decision theory, admissibility, and Bayes rules. prereq: [5102 or 8102 or instr consent], [[Math 5615, Math 5616] or real analysis], matrix algebra

STAT 8311 - Linear Models

Credits:	3.0 [max 4.0]
Typically offered:	Every Fall

General linear model theory from a coordinate-free geometric approach. Distribution theory, ANOVA tables, testing, confidence statements, mixed models, covariance structures, variance components estimation. prereq: Linear algebra, 5102 or 8102 or instr consent

VMED 5442 - Quantitative Methods for Population Health

Credits:	3.0 [max 6.0]
Typically offered:	Spring Odd Year

This course reviews the principles and application of advanced methods for analysis of population health data, with a focus on animal health and infectious diseases. Analytical techniques that will be taught and applied during the course include risk assessment, spatial analysis, disease modeling, and disease economics.

VMED 5915 - Essential Statistics for Life Sciences

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

This course is a broad overview of the principles and methods of statistical analysis used in life sciences research, including biological, veterinary, and translational research, and provides the background a new researcher needs to understand and apply commonly used statistical methods and the preparation needed for more advanced coursework. Classes will include general instruction and background information, detailed examples of how to perform the analyses, with actual data sets, and discussion on how the topic has been applied in biological research, including reading and assessing papers in the field. Computing will be performed using the R software environment, though students may use alternate software with permission. Topics will include: � Descriptive statistics and exploratory graphics � Understanding statistical inference and interpreting P-values and confidence intervals. � One and two sample inference, including t-tests, proportion tests, and non-parametric alternatives � Linear regression, including the effects of confounders � ANOVA methods, including pairwise comparisons and multiple comparisons

BMEN 4013 - CAD of Biomechanical/transport Devices

Credits:	1.0 [max 1.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Introduction to CAD modeling/analysis for medical device engineers using SOLIDWORKS CAD platform. Emphasis on practical applications of CAD for engineers using real-world examples from actual industry projects. prereq: BME Upper Division or instr consent

CHEN 8754 - Systems Analysis of Biological Processes

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Relating biological processes at molecular level to physiological level of cells/organisms/populations. Methodology for analyzing data. Quantification of molecular interplays. prereq: Grad student in [life sciences or chemical/physical sciences or engineering]; ChEn students must take A/F

CSCI 4041 - Algorithms and Data Structures

Credits:	4.0 [max 4.0]
Course Equivalencies:	CSci 4041/CSci 4041H
Typically offered:	Every Fall & Spring

Rigorous analysis of algorithms/implementation. Algorithm analysis, sorting algorithms, binary trees, heaps, priority queues, heapsort, balanced binary search trees, AVL trees, hash tables and hashing, graphs, graph traversal, single source shortest path, minimum cost spanning trees. prereq: [(1913 or 1933) and 2011] or instr consent; cannot be taken for grad CSci cr

CSCI 5106 - Programming Languages

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Design and implementation of high-level languages. Course has two parts: (1) language design principles, concepts, constructs; (2) language paradigms, applications. Note: course does not teach how to program in specific languages. prereq: 4011 or instr consent

CSCI 5115 - User Interface Design, Implementation and Evaluation

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Theory, design, programming, and evaluation of interactive application interfaces. Human capabilities and limitations, interface design and engineering, prototyping and interface construction, interface evaluation, and topics such as data visualization and World Wide Web. Course is built around a group project. prereq: 4041 or instr consent

CSCI 5161 - Introduction to Compilers

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Techniques for translating modern programming languages to intermediate forms or machine-executable instructions/their organization into compiler. Lexical analysis, syntax analysis, semantic analysis, data flow analysis, code generation. Compiler project for prototypical language. prereq: [2021, 5106] or instr consent

CSCI 5204 - Advanced Computer Architecture

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 5204/EE 5364
Typically offered:	Every Fall

Instruction set architecture, processor microarchitecture, memory, I/O systems. Interactions between computer software and hardware. Methodologies of computer design. prereq: 4203 or EE 4363

CSCI 5302 - Analysis of Numerical Algorithms

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Additional topics in numerical analysis. Interpolation, approximation, extrapolation, numerical integration/differentiation, numerical solutions of ordinary differential equations. Introduction to optimization techniques. prereq: 2031 or 2033 or instr consent

CSCI 5421 - Advanced Algorithms and Data Structures

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains. prereq: 4041 or instr consent

CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads. prereq: 4041 or instr consent

CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Computational methods for analyzing, integrating, and deriving predictions from genomic/proteomic data. Analyzing gene expression, proteomic data, and protein-protein interaction networks. Protein/gene function prediction, Integrating diverse data, visualizing genomic datasets. prereq: 3003 or 4041 or instr consent

CSCI 5465 - Introduction to Computing for Biologists

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 3003/CSci 5465
Typically offered:	Fall Odd Year

This course is designed for graduate students in biology or other related sciences that wish to learn fundamental computing skills that will enable them to develop their own computational approaches for meaningful interpretation of scientific data. Students will complete programming assignments in Python and R. No previous programming knowledge assumed. Prereq: Introductory biology course; non-CSE students only.

CSCI 5481 - Computational Techniques for Genomics

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Techniques to analyze biological data generated by genome sequencing, proteomics, cell-wide measurements of gene expression changes. Algorithms for single/multiple sequence alignments/assembly. Search algorithms for sequence databases, phylogenetic tree construction algorithms. Algorithms for gene/promoter and protein structure prediction. Data mining for micro array expression analysis. Reverse engineering of regulatory networks. prereq: 4041 or instr consent

CSCI 5511 - Artificial Intelligence I

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 4511W/CSci 5511
Prerequisites:	[2041 or #], grad student
Typically offered:	Every Fall

Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language. prereq: [2041 or instr consent], grad student

CSCI 5512 - Artificial Intelligence II

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 5512W/CSci 5512
Typically offered:	Every Spring

Uncertainty in artificial intelligence. Probability as a model of uncertainty, methods for reasoning/learning under uncertainty, utility theory, decision-theoretic methods. prereq: [STAT 3021, 4041] or instr consent

CSCI 5521 - Machine Learning Fundamentals

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives

CSCI 5523 - Introduction to Data Mining

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent

CSCI 5525 - Machine Learning: Analysis and Methods

Credits:	3.0 [max 3.0]
Typically offered:	Fall Even Year

Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent

CSCI 5561 - Computer Vision

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Issues in perspective transformations, edge detection, image filtering, image segmentation, and feature tracking. Complex problems in shape recovery, stereo, active vision, autonomous navigation, shadows, and physics-based vision. Applications. prereq: CSci 5511, 5521, or instructor consent.

CSCI 5609 - Visualization

Credits:	3.0 [max 3.0]
Typically offered:	Fall Even Year

Fundamental theory/practice in data visualization. Programming applications. Perceptual issues in effective data representation, multivariate visualization, information visualization, vector field/volume visualization. prereq: [1913, 4041] or equiv or instr consent

CSCI 5619 - Virtual Reality and 3D Interaction

Credits:	3.0 [max 3.0]
Typically offered:	Spring Odd Year

Introduction to software, technology/applications in virtual/augmented reality, 3D user interaction. Overview of current research. Hands-on projects. prereq: 4611 or 5607 or 5115 or equiv or instr consent

CSCI 5707 - Principles of Database Systems

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 4707/CSci 5707/INET 4707
Typically offered:	Every Fall

Concepts, database architecture, alternative conceptual data models, foundations of data manipulation/analysis, logical data models, database designs, models of database security/integrity, current trends. prereq: [4041 or instr consent], grad student

CSCI 5708 - Architecture and Implementation of Database Management Systems

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Techniques in commercial/research-oriented database systems. Catalogs. Physical storage techniques. Query processing/optimization. Transaction management. Mechanisms for concurrency control, disaster recovery, distribution, security, integrity, extended data types, triggers, and rules. prereq: 4041 or 4707 or 5707 or instr. consent

CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science

Credits:	3.0 [max 3.0]
Typically offered:	Spring Even Year

Spatial databases and querying, spatial big data mining, spatial data-structures and algorithms, positioning, earth observation, cartography, and geo-visulization. Trends such as spatio-temporal, and geospatial cloud analytics, etc. prereq: Familiarity with Java, C++, or Python

CSCI 5801 - Software Engineering I

Credits:	3.0 [max 3.0]
Prerequisites:	2041 or #
Typically offered:	Every Fall

Advanced introduction to software engineering. Software life cycle, development models, software requirements analysis, software design, coding, maintenance. prereq: 2041 or instr consent

CSCI 5980 - Special Topics in Computer Science

Credits:	1.0 -3.0 [max 27.0]
Typically offered:	Periodic Fall & Spring

Lectures and informal discussions on current topics in computer science. prereq: instr consent; may be repeated for cr

CSCI 8205 - Parallel Computer Organization

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 8205/EE 8367
Typically offered:	Every Spring

Design/implementation of multiprocessor systems. Parallel machine organization, system design. Differences between parallel, uniprocessor machines. Programming models. Synchronization/communication. Topologies, message routing strategies. Performance optimization techniques. Compiler, system software issues. prereq: 5204 or EE 5364 or instr consent

CSCI 8551 - Intelligent Agents

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

Theories of intelligent agents. Agent architectures; knowledge representation, communication, cooperation, and negotiation among multiple agents; planning and learning; issues in designing agents with a physical body; dealing with sensors and actuators; world modeling. prereq: 5511 or instr consent

CSCI 8715 - Spatial Data Science Research

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Motivation, models of spatial information, querying spatial data, processing strategies for spatial queries, multi-dimensional storage/access methods, spatial graph datasets, spatial data mining, trends (e.g., spatio-temporal databases, mobile objects, raster databases), research literature, how to pursue research. prereq: 4707 or 5707 or 5715 or GIS 5571 or GIS 5573

CSCI 8725 - Databases for Bioinformatics

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Spring

DBMS support for biological databases, data models. Searching integrated public domain databases. Queries/analyses, DBMS extensions, emerging applications. prereq: 4707 or 5707 or instr consent

EE 4389W - Introduction to Predictive Learning (WI)

Credits:	3.0 [max 3.0]
Typically offered:	Fall Odd Year

Empirical inference and statistical learning. Classical statistical framework, model complexity control, Vapnik-Chervonenkis (VC) theoretical framework, philosophical perspective. Nonlinear methods. New types of inference. Application studies. prereq: [3025, ECE student] or STAT 3022; computer programming or MATLAB or similar environment is recommended for ECE students

EE 5231 - Linear Systems and Control

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

The course studies finite-dimensional linear systems in continuous and discrete time. Such systems are described by ordinary differential and difference equations. Input-output and state-space descriptions are provided and analyzed. Introductory methods for controlling such systems are developed. prereq: [3015, CSE grad student] or instr consent

EE 5239 - Introduction to Nonlinear Optimization

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Nonlinear optimization. Analytical/computational methods. Constrained optimization methods. Convex analysis, Lagrangian relaxation, non-differentiable optimization, applications in integer programming. Optimality conditions, Lagrange multiplier theory, duality theory. Control, communications, management science applications. prereq: [3025, Math 2373, Math 2374, CSE grad student] or dept consent

EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Physics of computation will explore how physical principles and limits have been shaping paradigms of computing. A key goal of this course is to understand how (and to what extent) a paradigm shift in computing can help with emerging energy problems. Topics include physical limits of computing, coding and information theoretical foundations, computing with beyond-CMOS devices, reversible computing, quantum computing, stochastic computing. A previous course in computer architecture is suggested but not required.

EE 5393 - Circuits, Computation, and Biology

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Connections between digital circuit design and synthetic/computational biology. Probabilistic, discrete-event simulation. Timing analysis. Information-Theoretic Analysis. Feedback in digital circuits/genetic regulatory systems. Synthesizing stochastic logic and probabilistic biochemistry.

EE 5531 - Probability and Stochastic Processes

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Probability, random variables and random processes. System response to random inputs. Gaussian, Markov and other processes for modeling and engineering applications. Correlation and spectral analysis. Estimation principles. Examples from digital communications and computer networks. prereq: [3025, CSE grad student] or dept consent

EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence

Credits:	3.0 [max 3.0]
Course Equivalencies:	EE 5561/EE 8541
Typically offered:	Every Spring

Image enhancement, denoising, segmentation, registration, and computational imaging. Sampling, quantization, morphological processing, 2D image transforms, linear filtering, sparsity and compression, statistical modeling, optimization methods, multiresolution techniques, artificial intelligence concepts, neural networks and their applications in classification and regression tasks in image processing. Emphasis is on the principles of image processing. Implementation of algorithms in Matlab/Python and using deep learning frameworks. prereq: [4541, 5581, CSE grad student] or instr consent

EE 8591 - Predictive Learning from Data

Credits:	3.0 [max 3.0]
Typically offered:	Fall Even Year

Methods for estimating dependencies from data have been traditionally explored in such diverse fields as: statistics (multivariate regression and classification), engineering (pattern recognition, system identification), computer science (artificial intelligence, machine learning, data mining) and bioinformatics. Recent interest in learning methods is triggered by the widespread use of digital technology and availability of data. Unfortunately, developments in each field are seldom related to other fields. This course is concerned with estimation of predictive data-analytic models that are estimated using past data, but are used for prediction or decision making with new data. This course will first present general conceptual framework for learning predictive models from data, using Vapnik-Chervonenkis (VC) theoretical framework, and then discuss various methods developed in statistics, pattern recognition and machine learning. Course descriptions will emphasize methodological aspects of machine learning, rather than development of �new� algorithms. prereq: CSE grad student or instr consent

GCD 5005 - Computer Programming for Biology

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Computer programming skills with applications in biology. Design/build new computer programs for applications in cell/developmental biology, including modeling of biological processes, advanced data analysis, automated image analysis. prereq: BIOL 4003 or BIOL 4004 or GCD 3033 or CBS grad or BMBB or MCDB&G grad student, general statistics course

HINF 5430 - Foundations of Health Informatics I

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

An introductory survey of health informatics, focusing on foundational concepts. Topics covered include: conceptualizations of data, information, and knowledge; current terminologies, coding, and classification systems for medical information; ethics, privacy, and security; systems analysis, process and data modeling; human-computer interaction and data visualization. Lectures, readings, and exercises highlight the intersections of these topics with electronic health record systems and other health information technology. prereq: Junior, senior, grad student, professional student, or instr consent

HINF 5431 - Foundations of Health Informatics II

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

An introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research. prereq: Junior, senior, grad student, professional student, or instr consent

HINF 5440 - Foundations of Translational Bioinformatics

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Translational bioinformatics deals with the assaying, computational analysis and knowledge-based interpretation of complex molecular data to better understand, prevent, diagnose and treat disease. This course emphasizes deep DNA sequencing methods that have persistent impact on research related to disease diagnosis and treatment. The course covers sequence analysis, applications to genome sequences, and sequence-function analysis, analysis of modern genomic data, sequence analysis for gene expression/functional genomics analysis, and gene mapping/applied population genetics. Prerequisites: MS, PhD, or MD/PhD student interested in translational bioinformatics

HINF 5502 - Python Programming Essentials for the Health Sciences

Credits:	1.0 [max 1.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall & Spring

Computer programming essentials for health sciences/health care applications using Python 3. Intended for students with limited programming background, or students wishing to obtain proficiency in Python programming language. prereq: Junior or senior or grad student or professional student or instr consent

HINF 5510 - Applied Health Care Databases: Database Principles and Data Evaluation

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Principles of database theory, modeling, design, and manipulation of databases will be introduced, taught with a healthcare applications emphasis. Students will gain experience using a relational database management system (RDBMS), and database manipulation will be explored using Structured Query Language (SQL) to compose and execute queries. Students will be able to critically evaluate database query methods and results, and understand their implications for health care. prereq: Junior or senior or grad student or professional student or instr consent

HINF 5520 - Informatics Methods for Health Care Quality, Outcomes, and Patient Safety

Credits:	2.0 [max 2.0]
Typically offered:	Every Spring

Application/operation of clinical information systems, electronic health records, decision support/application in health care system. Use of clinical information systems/association with health care delivery, payment, quality, outcomes. prereq: Junior or senior or grad student or professional student or instr consent

HINF 5531 - Health Data Analytics and Data Science

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Data science methods and techniques for the extraction, preparation, and use of health data in decision making. prereq: Junior or senior or professional student or grad student or instr consent

HINF 5610 - Foundations of Biomedical Natural Language Processing

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

The course will provide a systematic introduction to basic knowledge and methods used in natural language processing (NLP) research. It will introduce biomedical NLP tasks and methods as well as their resources and applications in the biomedical domain. The course will also provide hands-on experience with existing NLP tools and systems. Students will gain basic knowledge and skills in handling with main biomedical NLP tasks. Prerequisites graduate student or instructor consent; Experience with at least one programming language (Python or Perl preferred) Recommended: basic understanding of data mining concepts, basic knowledge of computational linguistics

HINF 5620 - Data Visualization for the Health Sciences

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Periodic Spring

An advanced health informatics course, focusing on theoretical and practical aspects of data and information visualization for health care and the health sciences. Topics include classic and novel visualization types; models of human visual perception and cognition; color, text and typography; maps and diagrams; evaluation and testing; and the aesthetic and cultural aspects of visualization. Examples emphasize health sciences applications for clinicians, patients, researchers, and analysts. Modern programming and commercial tools are discussed, including D3, ggplot2, and Tableau. Students will report on and discuss visualization methods, published studies and books, culminating in a final visualization project of the student's choosing.

HINF 5650 - Integrative Genomics and Computational Methods

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Periodic Spring

Genome-scale high throughput data sets are a central feature of modern biological research and translational clinical study. Experimental, computational biologists and clinical researchers who want to get the most from their data sets need to have a firm grasp and understanding of genomic data structure characteristics, analytical methodology and the intrinsic connection to integrate. This course is designed to build competence in quantitative methods for the analysis of high-throughput genomic data and data integration.

HINF 8220 - Computational Causal Analytics

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Identifying causal relationships and mechanisms is the ultimate goal of natural sciences. This course will introduce concepts and techniques underlying computational causal discovery and causal inference utilizing both observational and experimental data. Example applications of the above mentioned techniques in the domain of health sciences include reconstructing the molecular pathways underlying a particular disease, identifying the complex and interacting factors influencing a mental health disorder, and evaluating the potential impact of a public health policy. The course emphasizes both on the theoretical foundations and the practical aspects of causal discovery and causal inference. Students will gain hands-on experience with applying major causal discovery algorithms on simulated and real data.

HINF 8430 - Foundations of Health Informatics I Lab

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

The PhD-level lab complement for introductory survey of health informatics, focusing on foundational concepts. Topics covered include: conceptualizations of data, information, and knowledge; current terminologies, coding, and classification systems for medical information; ethics, privacy, and security; systems analysis, process and data modeling; human-computer interaction and data visualization. Lectures, readings, and exercises highlight the intersections of these topics with electronic health record systems and other health information technology.

HINF 8440 - Foundations of Translational Bioinformatics Lab

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Translational bioinformatics deals with the assaying, computational analysis and knowledge-based interpretation of complex molecular data to better understand, prevent, diagnose and treat disease. This course emphasizes deep DNA sequencing methods that have persistent impact on research related to disease diagnosis and treatment. The course covers sequence analysis, applications to genome sequences, and sequence-function analysis, analysis of modern genomic data, sequence analysis for gene expression/functional genomics analysis, and gene mapping/applied population genetics. Prerequisites: MS, PhD, or MD/PhD student interested in translational bioinformatics

MBA 6241 - Competing in a Data-Driven Digital Age

Credits:	2.0 [max 2.0]
Course Equivalencies:	IDSc 6040/MBA 6241
Grading Basis:	A-F only
Typically offered:	Every Fall & Spring

Contemporary managers must understand how the convergence of mobility, analytics, social media, cloud computing, and AI are transforming firms, industries, markets, and society. This course provides tools and conceptual frameworks for competing in the digital age. Students will learn state-of-the-art skills in the context of digital disruption, platform based business models, Internet of Things, digital advertising, social networks, social media, big-data, and open innovation that pervade competition in the digital age. These will include the fundamentals of predictive modeling, large scale A/B testing, social networks analysis, and an exposure to the issues on the ethics and bias involved in AI applications. While this course will use case studies in the digital domain, the methods taught here have a wide range of applicability across functions and verticals in modern business environments. prereq: FT MBA, Mgmt Sci MBA or Online MBA student

PUBH 6325 - Data Processing with PC-SAS

Credits:	1.0 [max 1.0]
Typically offered:	Every Spring

Introduction to methods for transferring/processing existing data sources. Emphasizes hands-on approach to pre-statistical data processing and analysis with PC-SAS statistical software with a Microsoft Windows operating system.

PUBH 6420 - Introduction to SAS Programming

Credits:	1.0 [max 1.0]
Typically offered:	Periodic Fall & Summer

Use of SAS for analysis of biomedical data. Data manipulation/description. Basic statistical analyses (t-tests, chi-square, simple regression).

PUBH 6717 - Decision Analysis for Health Care

Credits:	2.0 [max 2.0]
Typically offered:	Every Fall

Introduction to methods/range of applications of decision analysis and cost-effectiveness analysis in health care technology assessment, medical decision making, and health resource allocation.

PUBH 6813 - Managing Electronic Health Information

Credits:	2.0 [max 2.0]
Grading Basis:	OPT No Aud
Typically offered:	Every Spring

Managing health information is a central function of health care organizations. Information is used for managing population health, profiling providers, and measuring quality. This course describes relational data theory, normalization, and Structured Query Language (SQL) will be used to create and query databases. Students will be introduced to the basic programming skills necessary to manage data in research projects. Programming aspects of the course will use SQL procedure in the SAS language. prereq: Admission to a University of Minnesota Masters program or Permission of instructor.

PUBH 7461 - Exploring and Visualizing Data in R

Credits:	2.0 [max 2.0]
Typically offered:	Every Fall

This course is intended for students, both within and outside the School of Public Health, who want to learn how to manipulate data, perform simple statistical analyses, and prepare basic visualizations using the statistical software R. While the tools and techniques taught will be generic, many of the examples will be drawn from biomedicine and public health.

PUBH 7462 - Advanced Programming and Data Analysis in R

Credits:	2.0 [max 2.0]
Typically offered:	Every Spring

This course is intended for students who are relatively proficient with R, and are looking to improve their coding and data analysis skills. The emphasis will be on learning tools and techniques which are useful to students who will be doing non-trivial programming and/or data analysis in either a research or production environment.

PUBH 7475 - Statistical Learning and Data Mining

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Spring

Various statistical techniques for extracting useful information (i.e., learning) from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles, unsupervised learning. prereq: [[[6450, 6452] or equiv], programming backgroud in [FORTRAN or C/C++ or JAVA or Splus/R]] or instr consent; 2nd yr MS recommended

SENG 5199 - Topics in Software Engineering

Credits:	2.0 -3.0 [max 6.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Topics specified in Class Schedule. prereq: SEng grad student

SENG 5831 - Software Development for Real-Time Systems

Credits:	2.0 -3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Periodic Fall

Analysis, design, verification, and validation of real-time systems. Periodic, aperiodic, and sporadic processes, scheduling theory. Pragmatic issues. prereq: Grad SEng major

VMED 5181 - Spatial Analysis in Infectious Disease Epidemiology

Credits:	3.0 [max 3.0]
Grading Basis:	OPT No Aud
Typically offered:	Every Spring

Spatial distribution of disease events. Exposures/outcomes. Factors that determine where diseases occur. Analyzing spatial disease data in public health, geography, epidemiology. Focuses on human/animal health related examples. prereq: Intro to epidemiology, statistics,

AGRO 5021 - Plant Breeding Principles

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

This course is intended for advanced undergraduate students and graduate students that are either: 1) not plant breeding majors who will benefit from a basic understanding of how genetics is applied to plant improvement; or 2) plant breeding majors lacking prior coursework in plant breeding. The objective of this course is to develop an understanding of the underlying principles, ideas, and concepts important to applying genetic principles to plant breeding, evaluating breeding methods, and enhancing genetic progress and efficiency.

AGRO 5121 - Applied Experimental Design

Credits:	4.0 [max 4.0]
Course Equivalencies:	Agro 5121/Ent 5121
Typically offered:	Every Spring

Principles of sampling methodologies, experimental design, and statistical analyses. Methods/procedures in generating scientific hypotheses. Organizing, initiating, conducting, and analyzing scientific experiments using experimental designs and statistical procedures. prereq: Stat 5021 or equiv or instr consent

AGRO 5311 - Research Methods in Crop Improvement and Production

Credits:	1.0 [max 1.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall & Summer

Demonstrations and discussions of techniques in crop improvement and/or production research. Presentations integrate biotechnology with traditional breeding methods; production sessions emphasize ecologically sound cropping systems. prereq: applied plant sciences grad

BIOC 5002 - Critical Evaluation of Biochemistry Research

Credits:	1.0 [max 1.0]
Course Equivalencies:	BioC 3690/BioC 5002
Grading Basis:	S-N only
Typically offered:	Every Fall & Spring

BioC 5002 guides advanced undergraduates and new graduate students as they learn how to design experiments and to critically evaluate a wide variety of cutting-edge research projects, both as readers and as researchers. Introductory lectures include peer review, experimental design, critical thinking and the psychology of judgment and decision-making. This is followed by a series of guest speakers who will guide students as they develop their skills in evaluation of current research papers.

BIOC 5216 - Current Topics in Signal Transduction

Credits:	2.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Spring

Mechanisms by which biological signals evoke biochemical responses.

BIOC 5309 - Biocatalysis and Biodegradation

Credits:	3.0 [max 3.0]
Course Equivalencies:	Bioc 5309/MicE 5309
Typically offered:	Every Spring

Fundamentals of microbial enzymes/metabolism as pertaining to biodegradation of environmental pollutants/biosynthesis for making commodity chemicals. Practical examples. Guest speakers from industry.

BIOC 5351 - Protein Engineering

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Key properties of enzymes/molecular basis, computer modeling strategies, mutagenesis strategies to create protein variants, expression/screening of protein variants. Evaluate research papers, identify unsolved practical/theoretical problems, plan protein engineering experiment.

BIOC 5352 - Biotechnology and Bioengineering for Biochemists

Credits:	3.0 [max 3.0]
Course Equivalencies:	BioC 5352/MicB 5352
Grading Basis:	A-F or Aud
Typically offered:	Periodic Spring

Protein biotechnology. Microorganisms used as hosts for protein expression, protein expression, and engineering methods. Production of enzymes of industrial interest. Applications of protein biotechnology in bioelectronics. Formulation of therapeutic biopharmaceuticals. Recommended prerequisites: Biochemistry (BiOC 3021 or 3022 or 4331) and Microbiology MICB 3301

BIOC 5361 - Microbial Genomics and Bioinformatics

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

Introduction to genomics. Emphasizes microbial genomics. Sequencing methods, sequence analysis, genomics databases, genome mapping, prokaryotic horizontal gene transfer, genomics in biotechnology, intellectual property issues. Hands-on introduction to UNIX shell scripting, genomic data analysis using R and Excel in a computer lab setting. prereq: College-level courses in [organic chemistry, biochemistry, microbiology]

BIOC 5444 - Muscle

Credits:	3.0 [max 3.0]
Course Equivalencies:	BioC 5444/ Phsl 5444
Typically offered:	Every Spring

Muscle molecular structure/function and disease. Muscle regulation, ion transport, and force generation. Muscular dystrophy and heart disease. prereq: 3021 or BIOL 3021 or 4331 or BIOL 4331 or PHSL 3061 or instr consent

BIOC 5528 - Spectroscopy and Kinetics

Credits:	4.0 [max 4.0]
Typically offered:	Every Spring

Biochemical dynamics from perspectives of kinetics and spectroscopy. Influence of structure, molecular interactions, and chemical transformations on biochemical reactions. Focuses on computational, spectroscopic, and physical methods. Steady-state and transient kinetics. Optical and magnetic resonance spectroscopies. prereq: Intro physical chemistry or equiv; intro biochemistry recommended

BIOC 6021 - Biochemistry

Credits:	3.0 [max 3.0]
Course Equivalencies:	BioC 3021/BioC 3022/BioC 4331/
Typically offered:	Every Fall, Spring & Summer

Fundamentals of biochemistry. Structure/function of proteins, nucleic acids, lipids and carbohydrates. Metabolism, regulation of metabolism. Quantitative treatments of chemical equilibria, enzyme catalysis, and bioenergetics. Chemical basis of genetic information flow. prereq: general biology, organic chemistry, instr consent; intended for MBS students

BIOC 8005 - Biochemistry: Structure and Catalysis

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Protein structure, methods to determine structure, protein folding, forces stabilizing macromolecular structure, protein engineering, design. Dynamic properties of proteins/enzymes, enzyme substrate complexes, mechanism of enzyme catalysis.

BIOC 8006 - Biochemistry: Metabolism and Control

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Enzymology of metabolism, metabolic regulation, metabolic control and cell signaling.

BIOC 8007 - Molecular Biology of the Genome

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

This course explores the molecular biology of the eukaryotic genome and transcriptome, focusing on fundamental genetic processes, molecular mechanisms, and their relationships to biology and disease. Students gain a firm understanding of the key concepts and techniques through lectures, reading, and discussions. Students learn to critically analyze scientific papers through student-led presentations and discussions. They gain experience in articulating scientific questions, formulating testable hypotheses, and designing experiments. This course promotes development of science writing skills.

BIOC 8008 - Molecular Biology of the Transcriptome

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

This course explores the molecular biology of the eukaryotic genome and transcriptome, focusing on fundamental genetic processes, molecular mechanisms, and their relationships to biology and disease. Students gain a firm understanding of the key concepts and techniques through lectures, reading, and discussions. Students learn to critically analyze scientific papers through student-led presentations and discussions. They gain experience in articulating scientific questions, formulating testable hypotheses, and designing experiments. This course promotes development of science writing skills.

BIOC 8084 - Research and Literature Reports

Credits:	1.0 [max 5.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall & Spring

Current developments. prereq: Grad BMBB major or instr consent

BIOC 8102 - Hot Topics in the Biology of Aging

Credits:	1.0 [max 1.0]
Typically offered:	Spring Odd Year

This course is intended to provide a platform of understanding about the major issues surrounding biological research in aging. This course will include a combination of student- and faculty-led discussions on select research topics that are highly relevant to the field of biogerontology research, along with instruction/discussions on scientific integrity. Student participants will lead discussions focused on their area of research expertise, utilizing a combination of review articles and research articles. Discussion of scientific misconduct will include case studies. This course is open to graduate students and post-doctoral fellows involved in the National Institutes on Aging (NIA) training grant ?Functional Proteomics of Aging?. This course is also open to other graduate students or post-doctoral fellows who are conducting biological research in aging with instructor?s permission.

BIOC 8184 - Graduate Seminar

Credits:	1.0 [max 5.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall & Spring

Reports on recent developments in the field and on research projects in the department. prereq: grad BMBB major or DGS consent

BIOL 4003 - Genetics

Credits:	3.0 [max 3.0]
Course Equivalencies:	Biol 4003/GCD 3022
Typically offered:	Every Fall, Spring & Summer

Genetic information, its transmission from parents to offspring, its expression in cells/organisms, and its course in populations. prereq: Biol 2003/2003H or BioC 3021 or BioC 4331 or grad

BIOL 5272 - Applied Biostatistics

Credits:	4.0 [max 3.0]
Course Equivalencies:	Biol 3272Biol 3272H//Biol 5272
Grading Basis:	A-F only
Typically offered:	Every Fall & Spring

Conceptual basis of statistical analysis. Statistical analysis of biological data. Data visualization, descriptive statistics, significance tests, experimental design, linear model, simple/multiple regression, general linear model. Lectures, computer lab. prereq: High school algebra; BIOL 2003 recommended.

BIOL 5950 - Special Topics

Credits:	1.0 -4.0 [max 8.0]
Typically offered:	Periodic Fall, Spring & Summer

In-depth study of special topic in life sciences.

BIOL 8100 - Improvisation for Scientists

Credits:	1.0 [max 1.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall

This is a 7-week course designed to practice a wide array of strategies in order to gain awareness and control over your personal expression. Students will develop more effective ways to expand their ability to navigate the stress generally associated with delivering content in front of others. By learning how to manage their personal expression more effectively, students will be able to use specific tools in order to adapt their expression to various settings (large audiences, small groups, or one on one interviews/counseling). Adapting exercises from techniques such as improvisation and storytelling, this class will provide a comfortable and safe environment for students who want to expand their confidence when presenting for others.

BMEN 4013 - CAD of Biomechanical/transport Devices

Credits:	1.0 [max 1.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Introduction to CAD modeling/analysis for medical device engineers using SOLIDWORKS CAD platform. Emphasis on practical applications of CAD for engineers using real-world examples from actual industry projects. prereq: BME Upper Division or instr consent

BMEN 5001 - Advanced Biomaterials

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Commonly used biomaterials. Chemical/physical aspects. Practical examples from such areas as cardiovascular/orthopedic applications, drug delivery, and cell encapsulation. Methods used for chemical analysis and for physical characterization of biomaterials. Effect of additives, stabilizers, processing conditions, and sterilization methods. prereq: 3301 or MatS 3011 or grad student or instr consent

BMEN 5041 - Tissue Engineering

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Fundamentals of wound healing and tissue repair; characterization of cell-matrix interactions; case study of engineered tissues, including skin, bone marrow, liver, vessel, and cartilage; regulation of biomaterials and engineered tissues. prereq: CSE upper div or grad student or med student or instr consent

BMEN 5101 - Advanced Bioelectricity and Instrumentation

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Spring

Instrumentation, computer systems, and processing requirements for clinical physiological signals. Electrode characteristics, signal processing, and interpretation of physiological events by ECG, EEG, and EMG. Measurement of respiration and blood volume/flow. prereq: [CSE upper div, grad student] or instructor consent

BMEN 5111 - Biomedical Ultrasound

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Introduction to biomedical ultrasound, including physics of ultrasound, transducer technology, medical ultrasound imaging, photoacoustic imaging, applications of non-linear acoustics, and high-intensity ultrasound. prereq: [[PHYS 1302 or equiv], [MATH 2374 or equiv]] or instr consent

BMEN 5201 - Advanced Biomechanics

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Introduction to biomechanics of musculoskeletal system. Anatomy, tissue material properties. Kinematics, dynamics, and control of joint/limb movement. Analysis of forces/motions within joints. Application to injury, disease. Treatment of specific joints, design of orthopedic devices/implants. prereq: [[3001 or equiv], [CSE upper div or grad student]] or instr consent

BMEN 5311 - Advanced Biomedical Transport Processes

Credits:	3.0 [max 3.0]
Course Equivalencies:	BMEn 5311/ChEn 5753/ME 5381
Typically offered:	Every Spring

Fluid flow and mass transfer in the body, bioreactors, and medical devices. Pulsatile flows. Flows around curved and deformable vessels. Boundary layer flows. Blood rheology. Interstitial (porous media) flows. Oxygenation. Cell migration. Student critiques of published papers.

BMEN 5321 - Microfluidics in Biology and Medicine

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Fundamentals of microfluidics. Fluid mechanics/transport phenomena in microscale systems. Pressure/surface driven flows. Capillary forces, electrokinetics, hydraulic circuit analysis. Finite element modeling for microfluidic systems. Design/fabrication methods for microfluidic devices. prereq: [3111, AEM 4201, ChEn 4005, [ME 3331 or ME 3332 or CSE grad student or instr consent]

BMEN 5351 - Cell Engineering

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Engineering approaches to cell-related phenomena important to cell/tissue engineering. Receptor/ligand binding. Trafficking/signaling processes. Applications to cell proliferation, adhesion, and motility. Cell-matrix interactions. prereq: [2401, [2501 or concurrent registration is required (or allowed) in 5501], [MATH 2243 or MATH 2373]] or CSE upper div or grad student or instr consent

BMEN 5411 - Neural Engineering

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Theoretical basis. Signal processing techniques. Modeling of nervous system, its response to stimulation. Electrode design, neural modeling, cochlear implants, deep brain stimulation. Prosthetic limbs, micturition control, prosthetic vision. Brain machine interface, seizure prediction, optical imaging of nervous system, place cell recordings in hippocampus. prereq: 3401 recommended

BMEN 5412 - Neuromodulation

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Fundamentals of bioengineering approaches to modulate the nervous system, including bioelectricity, biomagnetism, and optogenetics. Computational modeling, design, and physiological mechanisms of neuromodulation technologies. Clinical exposure to managing neurological disorders with neuromodulation technology.

BMEN 5413 - Neural Decoding and Interfacing

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Neural interface technologies currently in use in patients as well as the biophysical, neural coding, and hardware features relating to their implementation in humans. Practical and ethical considerations for implanting these devices into humans. prereq: CSE upper division student, CSE graduate student, or instructor approval. recommended: BMEn 3411

BMEN 5501 - Biology for Biomedical Engineers

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Concepts of cell/tissue structure/function. Basic principles of cell biology. Tissue engineering, artificial organs. prereq: Engineering upper div or grad student

BMEN 5701 - Cancer Bioengineering

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Cancer-specific cell, molecular/genetics events. Quantitative applications of bioinformatics/systems biology, optical imaging, cell/matrix mechanics. Drug transport (with some examination of design of novel therapeutics). prereq: [Upper division CSE undergraduate, CSE graduate student] or instr consent

BMEN 8101 - Biomedical Digital Signal Processing

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Signal processing theory for analyzing real world digital signals. Digital signal processing and mathematically derived algorithms for analysis of stochastic signals. Spectral analyses, noise cancellation, optimal filtering, blind source separation, beamforming techniques. prereq: [[MATH 2243 or MATH 2373], [MATH 2263 or MATH 2374]] or equiv

BMEN 8431 - Controlled Drug and Gene Delivery: Materials, Mechanisms, and Models

Credits:	4.0 [max 4.0]
Course Equivalencies:	BMEn 8431/PHM 8431
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Physical, chemical, physiological, mathematical principles underlying design of delivery systems for drugs. Small molecules, proteins, genes. Temporal controlled release. prereq: Differential equations course including partial differential equations or instr consent

CHEM 5210 - Materials Characterization

Credits:	4.0 [max 4.0]
Typically offered:	Every Spring

Modern tools/techniques for both bulk- and thin-film characterization. Topics may include ion-solid interactions, Rutherford back scattering, secondary ion mass spectrometry, solid-state NMR, x-ray photoelectron spectroscopy, small-angle x-ray/neutron scattering, transmission/scanning electron/probe microscopy, near-field scanning optical microscopy, porosimetry, adsorption techniques, and ellipsometry. prereq: grad student or instr consent

CHEM 5755 - X-Ray Crystallography

Credits:	4.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Essentials of crystallography as applied to modern, single crystal X-ray diffraction methods. Practical training in use of instrumentation in X-ray crystallography facility in Department of Chemistry. Date collection, correction/refinement, structure solutions, generation of publication materials, use of Cambridge Crystallographic Structure Database. prereq: Chem grad student or instr consent

CHEM 8011 - Mechanisms of Chemical Reactions

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Reaction mechanisms and methods of study. Mechanistic concepts in chemistry. Gas phase reactions to mechanisms, "electron pushing" mechanisms in organic reactions, mechanism of enzymatic reactions. Kinetic schemes and other strategies to investigate mechanisms. prereq: 2302 or equiv

CHEM 8021 - Computational Chemistry

Credits:	4.0 [max 4.0]
Typically offered:	Every Spring

Modern theoretical methods used in study of molecular structure, bonding, reactivity. Concepts/practical applications. Determination of spectra, relationship to experimental techniques. Molecular mechanics. Critical assessment of reliability of methods. prereq: 4502 or equiv

CHEM 8152 - Analytical Spectroscopy

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Survey of analytical spectroscopic methods. Design/application of spectroscopic instruments, including signal generation, acquisition, and interpretation. May include nuclear magnetic resonance, electron paramagnetic resonance, infrared and ultraviolet/visible spectroscopy, and mass spectrometry. prereq: grad chem major or instr consent

CHEM 8157 - Bioanalytical Chemistry

Credits:	4.0 [max 4.0]
Grading Basis:	A-F or Aud
Typically offered:	Periodic Spring

Theory and practical aspects of analytical methods used in determination/characterization of biologically important materials. Enzymatic/kinetic methods in study of proteins, carbohydrates, lipids, and nucleic acids.

CHEM 8411 - Introduction to Chemical Biology

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Chemistry of amino acids, peptides, proteins, lipids, carbohydrates, and nucleic acids. Structure, nomenclature, synthesis, and reactivity. Overview of techniques used to characterize these biomolecules. prereq: 2302 or equiv

CHEM 8412 - Chemical Biology of Enzymes

Credits:	4.0 [max 4.0]
Course Equivalencies:	Chem 8412/MedC 8412
Typically offered:	Periodic Spring

Enzyme classification with representative examples from current literature. Strategies used to decipher enzyme mechanisms. Chemical approaches for control of enzyme catalysis. prereq: 2302 or equiv

CHEM 8413 - Nucleic Acids

Credits:	4.0 [max 4.0]
Course Equivalencies:	Chem 8413/MedC 8413
Typically offered:	Periodic Fall

Chemistry and biology of nucleic acids: structure, thermodynamics, reactivity, DNA repair, chemical oligonucleotide synthesis, antisense approaches, ribozymes, overview of techniques used in nucleic acid research, interactions with small molecules and proteins. prereq: 2302 or equiv

CHEM 8541 - Dynamics

Credits:	4.0 [max 4.0]
Course Equivalencies:	Chem 5541/8541
Typically offered:	Periodic Fall

Mathematical methods for physical chemistry. Classical mechanics/dynamics, normal modes of vibration. Special topics such as rotational motion, Langevin equation, Brownian motion, time correlation functions, collision theory, cross sections, energy transfer, molecular forces, potential energy surfaces, classical electrostatics, Shannon entropy. prereq: Undergrad physical chem course

CHEM 8551 - Quantum Mechanics I

Credits:	4.0 [max 4.0]
Course Equivalencies:	Chem 5551/8551
Typically offered:	Every Fall

Review of classical mechanics. Postulates of quantum mechanics with applications to determination of single particle bound state energies and scattering cross-sections in central field potentials. Density operator formalism with applications to description of two level systems, two particle systems, entanglement, and Bell inequality. prereq: undergrad physical chem course

CHEM 8552 - Quantum Mechanics II

Credits:	2.0 [max 4.0]
Typically offered:	Every Spring

Second Quantization;Density matrices; Molecular Electronic Structure Theory; Hartree-Fock Theory; Electron Correlation; Configuration Interaction; Perturbation Theory; Energy Derivatives; Coupled-Cluster;Density Functional Theory; Relativistic Quantum Chemistry; prereq: 8551

CHEM 8561 - Thermodynamics, Statistical Mechanics, and Reaction Dynamics I

Credits:	4.0 [max 4.0]
Typically offered:	Every Fall

Two-part sequence. Thermodynamics, equilibrium statistical mechanics, ensemble theory, partition functions. Applications, including ideal gases/crystals. Theories of simple liquids, Monte Carlo, and molecular dynamics simulations. Reaction dynamics from microscopic viewpoint. prereq: undergrad physical chem course

CHEM 8565 - Chemical Reaction Dynamics

Credits:	2.0 [max 2.0]
Typically offered:	Periodic Spring

Fundamentals of chemical reaction dynamics including potential energy surfaces, collision theory, statistical mechanical background and transition state theory, variational transition state theory, activation energy, tunneling, unimolecular reactions, energy transfer, reactions in solution, solvation free energy, potential of mean force, quasithermodynamic treatment, reactions in solution, diffusion control, Kramers� theory, and photochemistry

CHEN 8754 - Systems Analysis of Biological Processes

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Relating biological processes at molecular level to physiological level of cells/organisms/populations. Methodology for analyzing data. Quantification of molecular interplays. prereq: Grad student in [life sciences or chemical/physical sciences or engineering]; ChEn students must take A/F

CMB 5912 - Creativity

Credits:	1.0 [max 1.0]
Typically offered:	Every Spring

Creativity will be explored and used to provide new perspectives on a variety of professional goals, activities and challenges. Lectures will be followed by a mixture of individual and group activities to provide a guided exploration of how these creative approaches can be applied to many situations. Students will learn skills to expand their vision, become more adept at problem solving, design more innovative research, inspire themselves and others and become more fascinating communicators.

CMB 8208 - Neuropsychopharmacology

Credits:	3.0 [max 3.0]
Course Equivalencies:	CMB 8208/ NSc 8208/Phcl 8208/P
Grading Basis:	A-F or Aud
Typically offered:	Fall Even Year

Relationships between drugs. Biochemical, behavioral, neurophysiological consequences. Functional biogenic amine, peptidergic, other pathways. Neuronal function/behavior. Feedback mechanisms, induction, inhibition. Stimulants, hallucinogens, depressants, opiates. Student presentations. prereq: graduate student and instr consent

CSCI 4041 - Algorithms and Data Structures

Credits:	4.0 [max 4.0]
Course Equivalencies:	CSci 4041/CSci 4041H
Typically offered:	Every Fall & Spring

Rigorous analysis of algorithms/implementation. Algorithm analysis, sorting algorithms, binary trees, heaps, priority queues, heapsort, balanced binary search trees, AVL trees, hash tables and hashing, graphs, graph traversal, single source shortest path, minimum cost spanning trees. prereq: [(1913 or 1933) and 2011] or instr consent; cannot be taken for grad CSci cr

CSCI 5115 - User Interface Design, Implementation and Evaluation

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Theory, design, programming, and evaluation of interactive application interfaces. Human capabilities and limitations, interface design and engineering, prototyping and interface construction, interface evaluation, and topics such as data visualization and World Wide Web. Course is built around a group project. prereq: 4041 or instr consent

CSCI 5161 - Introduction to Compilers

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Techniques for translating modern programming languages to intermediate forms or machine-executable instructions/their organization into compiler. Lexical analysis, syntax analysis, semantic analysis, data flow analysis, code generation. Compiler project for prototypical language. prereq: [2021, 5106] or instr consent

CSCI 5302 - Analysis of Numerical Algorithms

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Additional topics in numerical analysis. Interpolation, approximation, extrapolation, numerical integration/differentiation, numerical solutions of ordinary differential equations. Introduction to optimization techniques. prereq: 2031 or 2033 or instr consent

CSCI 5304 - Computational Aspects of Matrix Theory

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Perturbation theory for linear systems and eigenvalue problems. Direct/iterative solution of large linear systems. Matrix factorizations. Computation of eigenvalues/eigenvectors. Singular value decomposition. LAPACK/other software packages. Introduction to sparse matrix methods. prereq: 2031 or 2033 or instr consent

CSCI 5421 - Advanced Algorithms and Data Structures

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains. prereq: 4041 or instr consent

CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads. prereq: 4041 or instr consent

CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Computational methods for analyzing, integrating, and deriving predictions from genomic/proteomic data. Analyzing gene expression, proteomic data, and protein-protein interaction networks. Protein/gene function prediction, Integrating diverse data, visualizing genomic datasets. prereq: 3003 or 4041 or instr consent

CSCI 5465 - Introduction to Computing for Biologists

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 3003/CSci 5465
Typically offered:	Fall Odd Year

This course is designed for graduate students in biology or other related sciences that wish to learn fundamental computing skills that will enable them to develop their own computational approaches for meaningful interpretation of scientific data. Students will complete programming assignments in Python and R. No previous programming knowledge assumed. Prereq: Introductory biology course; non-CSE students only.

CSCI 5481 - Computational Techniques for Genomics

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Techniques to analyze biological data generated by genome sequencing, proteomics, cell-wide measurements of gene expression changes. Algorithms for single/multiple sequence alignments/assembly. Search algorithms for sequence databases, phylogenetic tree construction algorithms. Algorithms for gene/promoter and protein structure prediction. Data mining for micro array expression analysis. Reverse engineering of regulatory networks. prereq: 4041 or instr consent

CSCI 5511 - Artificial Intelligence I

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 4511W/CSci 5511
Prerequisites:	[2041 or #], grad student
Typically offered:	Every Fall

Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language. prereq: [2041 or instr consent], grad student

CSCI 5512 - Artificial Intelligence II

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 5512W/CSci 5512
Typically offered:	Every Spring

Uncertainty in artificial intelligence. Probability as a model of uncertainty, methods for reasoning/learning under uncertainty, utility theory, decision-theoretic methods. prereq: [STAT 3021, 4041] or instr consent

CSCI 5521 - Machine Learning Fundamentals

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives

CSCI 5523 - Introduction to Data Mining

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent

CSCI 5525 - Machine Learning: Analysis and Methods

Credits:	3.0 [max 3.0]
Typically offered:	Fall Even Year

Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent

CSCI 5561 - Computer Vision

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Issues in perspective transformations, edge detection, image filtering, image segmentation, and feature tracking. Complex problems in shape recovery, stereo, active vision, autonomous navigation, shadows, and physics-based vision. Applications. prereq: CSci 5511, 5521, or instructor consent.

CSCI 5609 - Visualization

Credits:	3.0 [max 3.0]
Typically offered:	Fall Even Year

Fundamental theory/practice in data visualization. Programming applications. Perceptual issues in effective data representation, multivariate visualization, information visualization, vector field/volume visualization. prereq: [1913, 4041] or equiv or instr consent

CSCI 5619 - Virtual Reality and 3D Interaction

Credits:	3.0 [max 3.0]
Typically offered:	Spring Odd Year

Introduction to software, technology/applications in virtual/augmented reality, 3D user interaction. Overview of current research. Hands-on projects. prereq: 4611 or 5607 or 5115 or equiv or instr consent

CSCI 5707 - Principles of Database Systems

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 4707/CSci 5707/INET 4707
Typically offered:	Every Fall

Concepts, database architecture, alternative conceptual data models, foundations of data manipulation/analysis, logical data models, database designs, models of database security/integrity, current trends. prereq: [4041 or instr consent], grad student

CSCI 5708 - Architecture and Implementation of Database Management Systems

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Techniques in commercial/research-oriented database systems. Catalogs. Physical storage techniques. Query processing/optimization. Transaction management. Mechanisms for concurrency control, disaster recovery, distribution, security, integrity, extended data types, triggers, and rules. prereq: 4041 or 4707 or 5707 or instr. consent

CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science

Credits:	3.0 [max 3.0]
Typically offered:	Spring Even Year

Spatial databases and querying, spatial big data mining, spatial data-structures and algorithms, positioning, earth observation, cartography, and geo-visulization. Trends such as spatio-temporal, and geospatial cloud analytics, etc. prereq: Familiarity with Java, C++, or Python

CSCI 5801 - Software Engineering I

Credits:	3.0 [max 3.0]
Prerequisites:	2041 or #
Typically offered:	Every Fall

Advanced introduction to software engineering. Software life cycle, development models, software requirements analysis, software design, coding, maintenance. prereq: 2041 or instr consent

CSCI 5980 - Special Topics in Computer Science

Credits:	1.0 -3.0 [max 27.0]
Typically offered:	Periodic Fall & Spring

Lectures and informal discussions on current topics in computer science. prereq: instr consent; may be repeated for cr

CSCI 8205 - Parallel Computer Organization

Credits:	3.0 [max 3.0]
Course Equivalencies:	CSci 8205/EE 8367
Typically offered:	Every Spring

Design/implementation of multiprocessor systems. Parallel machine organization, system design. Differences between parallel, uniprocessor machines. Programming models. Synchronization/communication. Topologies, message routing strategies. Performance optimization techniques. Compiler, system software issues. prereq: 5204 or EE 5364 or instr consent

CSCI 8551 - Intelligent Agents

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

Theories of intelligent agents. Agent architectures; knowledge representation, communication, cooperation, and negotiation among multiple agents; planning and learning; issues in designing agents with a physical body; dealing with sensors and actuators; world modeling. prereq: 5511 or instr consent

CSCI 8715 - Spatial Data Science Research

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Motivation, models of spatial information, querying spatial data, processing strategies for spatial queries, multi-dimensional storage/access methods, spatial graph datasets, spatial data mining, trends (e.g., spatio-temporal databases, mobile objects, raster databases), research literature, how to pursue research. prereq: 4707 or 5707 or 5715 or GIS 5571 or GIS 5573

CSCI 8725 - Databases for Bioinformatics

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Spring

DBMS support for biological databases, data models. Searching integrated public domain databases. Queries/analyses, DBMS extensions, emerging applications. prereq: 4707 or 5707 or instr consent

CSCI 8970 - Computer Science Colloquium

Credits:	1.0 [max 1.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall & Spring

Recent developments in computer science and related disciplines. Students must attend 13 of the 15 lectures.

CSCI 8991 - Independent Study

Credits:	1.0 -3.0 [max 9.0]
Typically offered:	Every Fall & Spring

Independent study with professor. prereq: instr consent

CSPH 5101 - Introduction to Integrative Healing Practices

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall, Spring & Summer

By the end of the course, students will demonstrate an understanding of the overall field of integrative healing practices, which includes both integrative and alternative (CAM) therapies. The course will cover theoretical framework, safety, efficacy, and evidence for various therapies and practices. The online version of this course is an approved 1Health Interprofessional Education (IPE) activity. prereq: Jr or sr or grad student; or instructor consent

CSPH 5421 - Botanical Medicines in Integrative Healthcare

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Botanical medicines have been used since ancient times in many cultures yet it is still not a significant part of what is considered ?traditional? medicine in our current healthcare model in the United States. Yet there is a growing interest among people in the U.S. looking for alternative treatments for a variety of common illnesses due to concerns of safety, efficacy, and a desire for more ?natural? products than more conventional pharmaceuticals. However, despite this growing interest, healthcare providers may have little to no knowledge regarding botanical medicines in regards to their therapeutic properties, efficacy (or lack thereof), and/or adverse effects. This is further compounded by a wealth of information on botanical medicines in the media and internet, much of which may be misleading and can lead to confusion regarding botanical medicines. The goal of this course is to learn basic properties and preparations of the most common botanical medicines in addition to their therapeutic effects for common disease states. Students will also learn about regulations, quality control, and safety concerns regarding use of botanical medicines. Included in this course is a discussion on the frequently overlooked botanical medicine we use everyday: our food! Relevant plant-based foods will be discussed periodically throughout the course to provide a practical application of the material learned in this course. prereq: Jr or sr or grad student, or instructor consent

DSCI 8970 - Data Science M.S. Colloquium

Credits:	1.0 [max 1.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall

Recent developments in Data Science and related disciplines. Students must attend 13 of the 15 lectures.

ECP 5220 - Regulatory Issues in Drug Research

Credits:	2.0 [max 2.0]
Typically offered:	Every Fall

Regulatory issues encountered in conducting drug research trials. Performing different aspects of clinical trials. Lectures, readings, small group discussions, homework assignments. prereq: ECP grad student or Pharm.D. professional student or instr consent

ECP 5620 - Drug Metabolism and Disposition

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Spring Odd Year

Oxidatative/conjugative enzymes systems involved in human drug metabolism/disposition. Various in vitro models used to evaluate drug metabolism or chemical entity, pros/cons of each. Factors involved in conducting in vivo studies. Components used to predict in vivo drug disposition from in vivo studies. prereq: Grad student or instr consent

ECP 8230 - Principles of Clinical Pharmacology

Credits:	2.0 [max 2.0]
Grading Basis:	A-F only
Typically offered:	Every Fall

Factors determining drug exposure, drug-receptor pharmacology, drug response. Personalized medicine including drug interactions, obesity, age (geriatrics/pediatrics), critical illness, therapeutic evaluation, drug development. prereq: Grad student in Experimental and Clinical Pharmacology or instr consent

ECP 8500 - Advances in Pharmacometrics Modeling and Simulation

Credits:	1.0 [max 6.0]
Grading Basis:	S-N only
Typically offered:	Every Fall & Spring

Modeling/simulation at interface between physiological/pharmacological processes. Current literature, discussion groups. Computer applications using relevant software programs. prereq: Grad student in ECP or PHM or instr consent

ECP 8503 - Intermediate Population PK/PD Methods

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Periodic Fall & Spring

This course will present the theory and hands-on application of intermediate population methods using nonlinear mixed-effects model applied to pharmacologic systems.

EE 4389W - Introduction to Predictive Learning (WI)

Credits:	3.0 [max 3.0]
Typically offered:	Fall Odd Year

Empirical inference and statistical learning. Classical statistical framework, model complexity control, Vapnik-Chervonenkis (VC) theoretical framework, philosophical perspective. Nonlinear methods. New types of inference. Application studies. prereq: [3025, ECE student] or STAT 3022; computer programming or MATLAB or similar environment is recommended for ECE students

EE 5231 - Linear Systems and Control

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

The course studies finite-dimensional linear systems in continuous and discrete time. Such systems are described by ordinary differential and difference equations. Input-output and state-space descriptions are provided and analyzed. Introductory methods for controlling such systems are developed. prereq: [3015, CSE grad student] or instr consent

EE 5239 - Introduction to Nonlinear Optimization

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Nonlinear optimization. Analytical/computational methods. Constrained optimization methods. Convex analysis, Lagrangian relaxation, non-differentiable optimization, applications in integer programming. Optimality conditions, Lagrange multiplier theory, duality theory. Control, communications, management science applications. prereq: [3025, Math 2373, Math 2374, CSE grad student] or dept consent

EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

Physics of computation will explore how physical principles and limits have been shaping paradigms of computing. A key goal of this course is to understand how (and to what extent) a paradigm shift in computing can help with emerging energy problems. Topics include physical limits of computing, coding and information theoretical foundations, computing with beyond-CMOS devices, reversible computing, quantum computing, stochastic computing. A previous course in computer architecture is suggested but not required.

EE 5393 - Circuits, Computation, and Biology

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Connections between digital circuit design and synthetic/computational biology. Probabilistic, discrete-event simulation. Timing analysis. Information-Theoretic Analysis. Feedback in digital circuits/genetic regulatory systems. Synthesizing stochastic logic and probabilistic biochemistry.

EE 5531 - Probability and Stochastic Processes

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Probability, random variables and random processes. System response to random inputs. Gaussian, Markov and other processes for modeling and engineering applications. Correlation and spectral analysis. Estimation principles. Examples from digital communications and computer networks. prereq: [3025, CSE grad student] or dept consent

EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence

Credits:	3.0 [max 3.0]
Course Equivalencies:	EE 5561/EE 8541
Typically offered:	Every Spring

Image enhancement, denoising, segmentation, registration, and computational imaging. Sampling, quantization, morphological processing, 2D image transforms, linear filtering, sparsity and compression, statistical modeling, optimization methods, multiresolution techniques, artificial intelligence concepts, neural networks and their applications in classification and regression tasks in image processing. Emphasis is on the principles of image processing. Implementation of algorithms in Matlab/Python and using deep learning frameworks. prereq: [4541, 5581, CSE grad student] or instr consent

EE 5601 - Introduction to RF/Microwave Engineering

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall & Spring

Fundamentals of EM theory and transmission lines concepts. Transmission lines and network analysis. CAD tool. Lumped circuit component designs. Passive circuit components. Connectivity to central communication theme. prereq: [3601, CSE grad student] or dept consent

EE 5616 - Antennas: Theory, Analysis, and Design

Credits:	3.0 [max 3.0]
Course Equivalencies:	EE 4616/EE 5616
Typically offered:	Every Fall

With the widespread use of cell phones autonomous vehicles, and the coming of the Internet of Things, there is an increasing need to understand wireless communications and radar sensors. A key component of these systems is the antenna. The purpose of this course is to help the student develop knowledge in the area of antennas. This involves understanding the parameters that are used to characterize antennas and how these effect system performance. An important aspect of the course is to provide the student with an understanding of the operating principles behind the most commonly used antennas. This is followed with exposure to basic design principles. These can be used to perform antenna design or can be used as starting points for design using an electromagnetic simulator. As part of the course, students will be exposed to simulator use through homework assignments, and possibly, course project work. prereq: EE 3601 or equivalent

EE 5811 - Biological Instrumentation

Credits:	3.0 [max 3.0]
Typically offered:	Spring Odd Year

This course will cover the physics and technology of biological instruments. The operating principles of optical, electrical, and mechanical biosensors will be discussed, followed by transport and delivery of biomolecules to the sensors. Techniques to manufacture these sensing devices, along with microfluidic packaging, will be covered. Lectures will be complemented by lab demo sessions to give students hands-on experiences in microfluidic chip fabrication, microscopy, and particle trapping experiments.

EE 8231 - Optimization Theory

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

Introduction to optimization in engineering; approximation theory. Least squares estimation, optimal control theory, and computational approaches. prereq: instr consent

EE 8591 - Predictive Learning from Data

Credits:	3.0 [max 3.0]
Typically offered:	Fall Even Year

Methods for estimating dependencies from data have been traditionally explored in such diverse fields as: statistics (multivariate regression and classification), engineering (pattern recognition, system identification), computer science (artificial intelligence, machine learning, data mining) and bioinformatics. Recent interest in learning methods is triggered by the widespread use of digital technology and availability of data. Unfortunately, developments in each field are seldom related to other fields. This course is concerned with estimation of predictive data-analytic models that are estimated using past data, but are used for prediction or decision making with new data. This course will first present general conceptual framework for learning predictive models from data, using Vapnik-Chervonenkis (VC) theoretical framework, and then discuss various methods developed in statistics, pattern recognition and machine learning. Course descriptions will emphasize methodological aspects of machine learning, rather than development of �new� algorithms. prereq: CSE grad student or instr consent

EEB 5042 - Quantitative Genetics

Credits:	3.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Fall

Fundamentals of quantitative genetics. Genetic/environmental influences on expression of quantitative traits. Approaches to characterizing genetic basis of trait variation. Processes that lead to change in quantitative traits. Applied/evolutionary aspects of quantitative genetic variation. prereq: [BIOL 4003 or GCD 3022] or instr consent; a course in statistics is recommended

GCD 4151 - Molecular Biology of Cancer

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Regulatory pathways involved in directing normal development of complex eukaryotic organisms, how disruptions of these pathways can lead to abnormal cell growth/cancer. Causes, detection, treatment, prevention of cancer. prereq: Biol 4003

GCD 5005 - Computer Programming for Biology

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Computer programming skills with applications in biology. Design/build new computer programs for applications in cell/developmental biology, including modeling of biological processes, advanced data analysis, automated image analysis. prereq: BIOL 4003 or BIOL 4004 or GCD 3033 or CBS grad or BMBB or MCDB&G grad student, general statistics course

GCD 5036 - Molecular Cell Biology

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall

Analysis of dynamic cellular activities at the molecular level in cell biological fields that are experiencing new research advances not yet reflected in textbooks. Significant emphasis is placed on understanding the experimental basis of our current knowledge of cellular processes through analysis of scientific papers. Project and presentation-based assessments of learning outcomes. prereq: BIOL 4004 or GCD 4005W or grad

GCD 8008 - Mammalian Gene Transfer and Genome Engineering

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Current gene transfer and genome engineering technology. Applications of genetic modifications in animals, particularly transgenic animals and human gene therapy. prereq: instr consent

GCD 8073 - Genetics & Genomics in Human Health

Credits:	2.0 [max 3.0]
Typically offered:	Every Spring

Application of molecular, biochemical, chromosomal, and population genetics to human variation and disease. Abnormal chromosome number and structure; abnormal enzyme, structural protein, receptor, and transport; analysis of inheritance patterns; behavioral genetics; genetic basis of common disease. Current research articles in human genetics. prereq: 8131 or BIOL 4003 or instr consent

GCD 8103 - Human Histology

Credits:	5.0 [max 5.0]
Course Equivalencies:	GCD 6103/8103
Typically offered:	Every Fall

Light/electron microscopic anatomy of tissues and their organization into human organs. Emphasizes integrating structure, its relationship to function at levels from molecules to organs. Lecture, lab. prereq: Undergraduate biology, chemistry, math, and physics course; instr consent

GCD 8131 - Advanced Molecular Genetics and Genomics

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

Literature-based course in modern molecular genetic and genomic analysis. Students will gain a deep understanding of the fundamental molecular mechanisms controlling inheritance in biological systems. Students will gain a facility in thinking critically and creatively about how genes work at cellular, organismal, and transgenerational levels. Course instruction emphasizes active-learning approaches, student presentations, and group projects. prereq: [3022 or BIOL 4003], [BIOC 3021 or BIOC 4331] or instr consent

GCD 8151 - Cellular Biochemistry and Cell Biology

Credits:	2.0 -4.0 [max 4.0]
Grading Basis:	A-F only
Typically offered:	Every Fall

This course introduces graduate students to fundamental concepts of Biochemical Unity (Part 1) and Cell Theory (Part 2). For Part 1, we will discuss matter of life, equilibrium, entropy & law of mass action, two state systems, random walks & diffusion, rate equations of chemical reactions, and explore how they relate to regulation of biological networks (gene regulation and signal transduction). For Part 2 we will focus on properties of biological membranes, membrane trafficking, protein import & degradation, nuclear structures and their function, as well as molecular motors, cytoskeletal dynamics, and mitosis. The course assumes students have had previous undergraduate courses in cell biology, biochemistry and genetics. prereq: [[[4034 or 8121 or BioC 8002], Biol 4004] or BMBB or MCDBG grad student] or instr consent

GCD 8161 - Advanced Cell Biology and Development

Credits:	2.0 [max 3.0]
Grading Basis:	A-F only
Typically offered:	Every Spring

The advanced cell and developmental biology of embryos, taught through in-depth, comparative analysis of historical and current primary research articles that illustrate developmental mechanisms and experimental approaches in key invertebrate and vertebrate model organisms. prereq:[BMBB or MCDBG grad student] or [GCD 4161, [GCD 8131 or Biol 4003], Biol 4004, and GCD 4034] or instr consent

GCD 8171 - Literature Analysis

Credits:	1.0 -2.0 [max 2.0]
Grading Basis:	A-F only
Typically offered:	Every Fall

Critical reading and evaluation of current literature. May include evaluation of both excellent and flawed papers. Intensive and in-depth discussions of selected papers in molecular biology, genetics, cell biology, and developmental biology. prereq: Grad MCDB&G or BMBB major

GCD 8920 - Special Topics

Credits:	1.0 -4.0 [max 4.0]
Typically offered:	Every Fall & Spring

Special topic shell

GRD 4999 - Graduate Summer Research

Credits:	0.0 [max 0.0]
Grading Basis:	No Grade
Typically offered:	Every Summer

Graduate Summer Research

HINF 5430 - Foundations of Health Informatics I

Credits:	3.0 [max 3.0]
Typically offered:	Every Fall & Spring

An introductory survey of health informatics, focusing on foundational concepts. Topics covered include: conceptualizations of data, information, and knowledge; current terminologies, coding, and classification systems for medical information; ethics, privacy, and security; systems analysis, process and data modeling; human-computer interaction and data visualization. Lectures, readings, and exercises highlight the intersections of these topics with electronic health record systems and other health information technology. prereq: Junior, senior, grad student, professional student, or instr consent

HINF 5431 - Foundations of Health Informatics II

Credits:	3.0 [max 3.0]
Typically offered:	Every Spring

An introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research. prereq: Junior, senior, grad student, professional student, or instr consent

HINF 5440 - Foundations of Translational Bioinformatics

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Translational bioinformatics deals with the assaying, computational analysis and knowledge-based interpretation of complex molecular data to better understand, prevent, diagnose and treat disease. This course emphasizes deep DNA sequencing methods that have persistent impact on research related to disease diagnosis and treatment. The course covers sequence analysis, applications to genome sequences, and sequence-function analysis, analysis of modern genomic data, sequence analysis for gene expression/functional genomics analysis, and gene mapping/applied population genetics. Prerequisites: MS, PhD, or MD/PhD student interested in translational bioinformatics

HINF 5496 - Internship in Health Informatics

Credits:	1.0 -6.0 [max 18.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall, Spring & Summer

Practical industrial experience not directly related to student's normal academic experience. prereq: HINF student or instr consent

HINF 5502 - Python Programming Essentials for the Health Sciences

Credits:	1.0 [max 1.0]
Grading Basis:	S-N or Aud
Typically offered:	Every Fall & Spring

Computer programming essentials for health sciences/health care applications using Python 3. Intended for students with limited programming background, or students wishing to obtain proficiency in Python programming language. prereq: Junior or senior or grad student or professional student or instr consent

HINF 5510 - Applied Health Care Databases: Database Principles and Data Evaluation

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

Principles of database theory, modeling, design, and manipulation of databases will be introduced, taught with a healthcare applications emphasis. Students will gain experience using a relational database management system (RDBMS), and database manipulation will be explored using Structured Query Language (SQL) to compose and execute queries. Students will be able to critically evaluate database query methods and results, and understand their implications for health care. prereq: Junior or senior or grad student or professional student or instr consent

HINF 5520 - Informatics Methods for Health Care Quality, Outcomes, and Patient Safety

Credits:	2.0 [max 2.0]
Typically offered:	Every Spring

Application/operation of clinical information systems, electronic health records, decision support/application in health care system. Use of clinical information systems/association with health care delivery, payment, quality, outcomes. prereq: Junior or senior or grad student or professional student or instr consent

HINF 5531 - Health Data Analytics and Data Science

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Data science methods and techniques for the extraction, preparation, and use of health data in decision making. prereq: Junior or senior or professional student or grad student or instr consent

HINF 5610 - Foundations of Biomedical Natural Language Processing

Credits:	3.0 [max 3.0]
Typically offered:	Periodic Fall

The course will provide a systematic introduction to basic knowledge and methods used in natural language processing (NLP) research. It will introduce biomedical NLP tasks and methods as well as their resources and applications in the biomedical domain. The course will also provide hands-on experience with existing NLP tools and systems. Students will gain basic knowledge and skills in handling with main biomedical NLP tasks. Prerequisites graduate student or instructor consent; Experience with at least one programming language (Python or Perl preferred) Recommended: basic understanding of data mining concepts, basic knowledge of computational linguistics

HINF 5620 - Data Visualization for the Health Sciences

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Periodic Spring

An advanced health informatics course, focusing on theoretical and practical aspects of data and information visualization for health care and the health sciences. Topics include classic and novel visualization types; models of human visual perception and cognition; color, text and typography; maps and diagrams; evaluation and testing; and the aesthetic and cultural aspects of visualization. Examples emphasize health sciences applications for clinicians, patients, researchers, and analysts. Modern programming and commercial tools are discussed, including D3, ggplot2, and Tableau. Students will report on and discuss visualization methods, published studies and books, culminating in a final visualization project of the student's choosing.

HINF 5650 - Integrative Genomics and Computational Methods

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Periodic Spring

Genome-scale high throughput data sets are a central feature of modern biological research and translational clinical study. Experimental, computational biologists and clinical researchers who want to get the most from their data sets need to have a firm grasp and understanding of genomic data structure characteristics, analytical methodology and the intrinsic connection to integrate. This course is designed to build competence in quantitative methods for the analysis of high-throughput genomic data and data integration.

HINF 8220 - Computational Causal Analytics

Credits:	3.0 [max 3.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Identifying causal relationships and mechanisms is the ultimate goal of natural sciences. This course will introduce concepts and techniques underlying computational causal discovery and causal inference utilizing both observational and experimental data. Example applications of the above mentioned techniques in the domain of health sciences include reconstructing the molecular pathways underlying a particular disease, identifying the complex and interacting factors influencing a mental health disorder, and evaluating the potential impact of a public health policy. The course emphasizes both on the theoretical foundations and the practical aspects of causal discovery and causal inference. Students will gain hands-on experience with applying major causal discovery algorithms on simulated and real data.

HINF 8430 - Foundations of Health Informatics I Lab

Credits:	2.0 [max 2.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall

The PhD-level lab complement for introductory survey of health informatics, focusing on foundational concepts. Topics covered include: conceptualizations of data, information, and knowledge; current terminologies, coding, and classification systems for medical information; ethics, privacy, and security; systems analysis, process and data modelin

University Catalogs

Bioinformatics and Computational Biology Minor