Twin Cities campus

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Twin Cities Campus

Data Science for Chemical Engineering and Materials Science M.S.

Chemical Engineering & Materials Science
College of Science and Engineering
Link to a list of faculty for this program.
Contact Information
Department of Chemical Engineering and Materials Science, University of Minnesota, 151 Amundson Hall, 421 Washington Avenue SE, Minneapolis, MN 55455 (612-625-0382; fax 612-626-7246)
Email: cemsds@umn.edu
Website: https://cse.umn.edu/cems
  • Program Type: Master's
  • Requirements for this program are current for Fall 2024
  • Length of program in credits: 30
  • This program does not require summer semesters for timely completion.
  • Degree: Master of Science
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
The M.S. in Data Science for Chemical Engineering and Materials Science degree bridges disciplinary expertise in chemical engineering and materials science with data and computational science. It aims to educate the next generation of chemical engineers and materials scientists that will be able to work seamlessly with digital technologies. The program core provides fundamental knowledge of statistical and data analysis, machine learning, and artificial intelligence, as well as their application in chemical, biological, and materials science and engineering problems. Elective courses allow students to specialize in artificial intelligence, high performance computing, systems engineering, automation and robotics, or data analytics, depending on their specific interests and needs.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.50.
Baccalaureate degree in Chemical Engineering, Materials Science or related field.
Other requirements to be completed before admission:
The undergraduate degree must include two semesters of calculus and one semester of the following: multivariable calculus; linear algebra/differential equations; statistics; programming in languages such as C++ or Python; and algorithms and data structures. Exceptions or substitutions will be considered on an individual basis.
Special Application Requirements:
Applications are accepted for fall semester only. The application deadline is January 15. Applications received after that date will be considered on an individual basis only under exceptional circumstances.
International applicants must submit score(s) from one of the following tests:
  • TOEFL
Key to test abbreviations (TOEFL).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Plan B: Plan B requires 30 major credits and 0 credits outside the major. The final exam is written and oral. A capstone project is required.
Capstone Project:Students complete a capstone project for a minimum of 3 credits under the supervision of faculty or in collaboration with industry advisors.
Plan C: Plan C requires 30 major credits and 0 credits outside the major. There is no final exam.
This program may be completed with a minor.
Use of 4xxx courses toward program requirements is permitted under certain conditions with adviser approval.
A minimum GPA of 2.80 is required for students to remain in good standing.
Courses must be taken on the A-F grade basis, unless only offered S/N, with a minimum grade of B- earned for each course. Approval of advisor and the director of graduate studies is required to apply 4xxx courses to degree requirements
Core Courses (19 credits)
Students are strongly recommended to take CSci 5521 before CHEN/MATS 5802. Students may complete one course from each of the following cross-listed pairs, but not both: CHEN/MATS 5801, 5802, 8201. Other courses may be selected with advisor and director of graduate studies approval.
CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
CSCI 5523 - Introduction to Data Mining (3.0 cr)
STAT 5302 - Applied Regression Analysis (4.0 cr)
CHEN 5801 - Optimization in Chemical and Energy Systems Engineering (3.0 cr)
or MATS 5801 - Optimization in Chemical and Energy Systems Engineering (3.0 cr)
CHEN 5802 - Applied Machine Learning in Chemical Engineering and Materials Science (3.0 cr)
or MATS 5802 - Applied Machine Learning in Chemical Engineering and Materials Science (3.0 cr)
CHEN 8201 - Applied Math (3.0 cr)
or MATS 8201 - Applied Math (3.0 cr)
Electives (8 to 11 credits)
Plan B students select at least 8 credits and Plan C students select at least 11 credits from the following. Other courses may be selected with advisor and director of graduate studies approval.
Major Electives
Students may complete one course from each of the following cross-listed pairs, but not both: CHEN/MATS 5771, 5803, 8001, 8221, 8301. Students who don't have an undergraduate degree in chemical engineering or materials science must take two courses from the following list, in consultation with the director of graduate studies:
CHEN 5751 - Biochemical Engineering (3.0 cr)
CHEN 5753 - Advanced Biomedical Transport Processes (3.0 cr)
CHEN 8101 - Fluid Mechanics (3.0 cr)
CHEN 8102 - Introduction to Rheology (3.0 cr)
CHEN 8104 - Coating Process Fundamentals (2.0 cr)
CHEN 8401 - Physical and Chemical Thermodynamics (3.0 cr)
CHEN 8402 - Statistical Thermodynamics and Kinetics (3.0 cr)
CHEN 8501 - Chemical Rate Processes: Analysis of Chemical Reactors (3.0 cr)
CHEN 8754 - Systems Analysis of Biological Processes (3.0 cr)
MATS 5517 - Microscopy of Materials (3.0 cr)
MATS 5531 {Inactive} (3.0 cr)
MATS 8002 - Thermodynamics and Kinetics (3.0 cr)
MATS 8003 - Electronic Properties (3.0 cr)
MATS 8004 - Mechanical Properties (3.0 cr)
MATS 8217 - Transmission Electron Microscopy (3.0 cr)
CHEN 5771 - Colloids and Dispersions (3.0 cr)
or MATS 5771 - Colloids and Dispersions (3.0 cr)
CHEN 5803 - Chemical and Materials Technology Commercialization (3.0 cr)
or MATS 5803 - Chemical and Materials Technology Commercialization (3.0 cr)
CHEN 8001 - Structure and Symmetry of Materials (3.0 cr)
or MATS 8001 - Structure and Symmetry of Materials (3.0 cr)
CHEN 8221 - Synthetic Polymer Chemistry (4.0 cr)
or MATS 8221 - Synthetic Polymer Chemistry (4.0 cr)
CHEN 8301 - Physical Rate Processes I: Transport (3.0 cr)
or MATS 8301 - Physical Rate Processes I: Transport (3.0 cr)
Outside Electives
Students must select at least one course from this list. Students may complete one course from each of the following cross listed pairs, but not both: AST 5731/STAT 5731, CSCI 8205/EE 8367, MATH 5651/STAT 5101, PUBH 8475/STAT 8056.
CSCI 5103 - Operating Systems (3.0 cr)
CSCI 5105 - Introduction to Distributed Systems (3.0 cr)
CSCI 5211 - Data Communications and Computer Networks (3.0 cr)
CSCI 5271 - Introduction to Computer Security (3.0 cr)
CSCI 5302 - Analysis of Numerical Algorithms (3.0 cr)
CSCI 5304 - Computational Aspects of Matrix Theory (3.0 cr)
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming (3.0 cr)
CSCI 5511 - Artificial Intelligence I (3.0 cr)
CSCI 5512 - Artificial Intelligence II (3.0 cr)
CSCI 5525 - Machine Learning: Analysis and Methods (3.0 cr)
CSCI 5551 - Introduction to Intelligent Robotic Systems (3.0 cr)
CSCI 5609 - Visualization (3.0 cr)
CSCI 5707 - Principles of Database Systems (3.0 cr)
CSCI 5708 - Architecture and Implementation of Database Management Systems (3.0 cr)
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science (3.0 cr)
CSCI 5751 - Big Data Engineering and Architecture (3.0 cr)
CSCI 5801 - Software Engineering I (3.0 cr)
CSCI 5802 - Software Engineering II (3.0 cr)
CSCI 8102 - Foundations of Distributed Computing (3.0 cr)
CSCI 8314 - Sparse Matrix Computations (3.0 cr)
CSCI 8581 - Big Data in Astrophysics (4.0 cr)
CSCI 8701 - Overview of Database Research (3.0 cr)
CSCI 8715 - Spatial Data Science Research (3.0 cr)
CSCI 8725 - Databases for Bioinformatics (3.0 cr)
CSCI 8735 - Advanced Database Systems (3.0 cr)
CSCI 8801 - Advanced Software Engineering (3.0 cr)
EE 5239 - Introduction to Nonlinear Optimization (3.0 cr)
EE 5251 - Optimal Filtering and Estimation (3.0 cr)
EE 5271 - Robot Vision (3.0 cr)
EE 5351 - Applied Parallel Programming (3.0 cr)
EE 5355 - Algorithmic Techniques for Scalable Many-core Computing (3.0 cr)
EE 5371 - Computer Systems Performance Measurement and Evaluation (3.0 cr)
EE 5389 - Introduction to Predictive Learning (3.0 cr)
EE 5501 - Digital Communication (3.0 cr)
EE 5531 - Probability and Stochastic Processes (3.0 cr)
EE 5542 - Adaptive Digital Signal Processing (3.0 cr)
EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence (3.0 cr)
EE 5581 - Information Theory and Coding (3.0 cr)
EE 5585 - Data Compression (3.0 cr)
EE 8231 - Optimization Theory (3.0 cr)
EE 8551 - Multirate Signal Processing and Applications (3.0 cr)
EE 5571 - Statistical Learning and Inference (3.0 cr)
EE 8591 - Predictive Learning from Data (3.0 cr)
IE 5531 - Engineering Optimization I (4.0 cr)
IE 5561 - Analytics and Data-Driven Decision Making (4.0 cr)
IE 8521 - Optimization (4.0 cr)
IE 8531 - Discrete Optimization (4.0 cr)
MSBA 6321 - Data Management, Databases, and Data Warehousing (3.0 cr)
MSBA 6331 - Big Data Analytics (3.0 cr)
PUBH 7401 - Fundamentals of Biostatistical Inference (4.0 cr)
PUBH 7402 - Biostatistics Modeling and Methods (4.0 cr)
PUBH 7405 - Biostatistical Inference I (4.0 cr)
PUBH 7406 - Biostatistical Inference II (3.0 cr)
PUBH 7407 - Analysis of Categorical Data (3.0 cr)
PUBH 7430 - Statistical Methods for Correlated Data (3.0 cr)
PUBH 7440 - Introduction to Bayesian Analysis (3.0 cr)
PUBH 7460 - Advanced Statistical Computing (3.0 cr)
PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)
PUBH 7485 - Methods for Causal Inference (3.0 cr)
PUBH 8401 - Linear Models (3.0 cr)
PUBH 8432 - Probability Models for Biostatistics (3.0 cr)
PUBH 8442 - Bayesian Decision Theory and Data Analysis (3.0 cr)
STAT 5052 - Statistical and Machine Learning (3.0 cr)
STAT 5102 - Theory of Statistics II (4.0 cr)
STAT 5201 - Sampling Methodology in Finite Populations (3.0 cr)
STAT 5303 - Designing Experiments (4.0 cr)
STAT 5401 - Applied Multivariate Methods (3.0 cr)
STAT 5421 - Analysis of Categorical Data (3.0 cr)
STAT 5511 - Time Series Analysis (3.0 cr)
STAT 5601 - Nonparametric Methods (3.0 cr)
STAT 5701 - Statistical Computing (3.0 cr)
STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods (3.0 cr)
STAT 8101 - Theory of Statistics 1 (3.0 cr)
STAT 8102 - Theory of Statistics 2 (3.0 cr)
STAT 8112 - Mathematical Statistics II (3.0 cr)
AST 5731 - Bayesian Astrostatistics (4.0 cr)
or STAT 5731 - Bayesian Astrostatistics (4.0 cr)
CSCI 8205 - Parallel Computer Organization (3.0 cr)
or EE 8367 - Parallel Computer Organization (3.0 cr)
MATH 5651 - Basic Theory of Probability and Statistics (4.0 cr)
or STAT 5101 - Theory of Statistics I (4.0 cr)
PUBH 8475 - Statistical Learning and Data Mining (3.0 cr)
or STAT 8056 - Statistical Learning and Data Mining (3.0 cr)
Plan Options
Plan B
Project Credits (3 credits)
Select 3 credits for the capstone project, in consultation with the advisor, from the following list:
CHEN 8993 - Directed Study (1.0-12.0 cr)
CHEN 8994 - Directed Research (1.0-12.0 cr)
MATS 8993 - Directed Study (1.0-12.0 cr)
MATS 8994 - Directed Research (1.0-12.0 cr)
 
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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
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.
CHEN 5801 - Optimization in Chemical and Energy Systems Engineering
Credits: 3.0 [max 3.0]
Course Equivalencies: MatS 5801 / CHEN 5801
Grading Basis: A-F or Aud
Typically offered: Every Fall
Mathematical optimization is a rigorous and systematic method for modeling and solving decision-making problems. It has become an indispensable tool in various disciplines, including economics, science, and engineering. In this course, students are introduced to the theory of mathematical optimization, systematic approaches to modeling complex optimization problems, and state-of-the-art algorithms for solving them. While the presented methods are general, we focus on applications in chemical engineering, energy systems engineering, and related disciplines. Many of the applications are directly related to the efficient design and operation of sustainable industrial systems.
MATS 5801 - Optimization in Chemical and Energy Systems Engineering
Credits: 3.0 [max 3.0]
Course Equivalencies: MatS 5801 / CHEN 5801
Grading Basis: A-F or Aud
Typically offered: Every Fall
Mathematical optimization is a rigorous and systematic method for modeling and solving decision-making problems. It has become an indispensable tool in various disciplines, including economics, science, and engineering. In this course, students are introduced to the theory of mathematical optimization, systematic approaches to modeling complex optimization problems, and state-of-the-art algorithms for solving them. While the presented methods are general, we focus on applications in chemical engineering, energy systems engineering, and related disciplines. Many of the applications are directly related to the efficient design and operation of sustainable industrial systems.
CHEN 5802 - Applied Machine Learning in Chemical Engineering and Materials Science
Credits: 3.0 [max 3.0]
Course Equivalencies: MatS 5802 / ChEn 5802
Grading Basis: A-F or Aud
Typically offered: Every Spring
Machine learning is an increasingly prominent tool used by engineers to aid in the design and characterization of materials and molecules. This course will introduce advanced undergraduates and graduate students to fundamental concepts and practical skills that enable the application of machine learning to these problems. These concepts and skills will be contextualized with examples of recent advances at the intersection of chemical engineering, materials science, and machine learning.
MATS 5802 - Applied Machine Learning in Chemical Engineering and Materials Science
Credits: 3.0 [max 3.0]
Course Equivalencies: MatS 5802 / ChEn 5802
Grading Basis: A-F or Aud
Typically offered: Every Spring
Machine learning is an increasingly prominent tool used by engineers to aid in the design and characterization of materials and molecules. This course will introduce advanced undergraduates and graduate students to fundamental concepts and practical skills that enable the application of machine learning to these problems. These concepts and skills will be contextualized with examples of recent advances at the intersection of chemical engineering, materials science, and machine learning.
CHEN 8201 - Applied Math
Credits: 3.0 [max 3.0]
Course Equivalencies: ChEn 4701/ChEn 8201
Grading Basis: A-F or Aud
Typically offered: Every Fall
Integrated approach to solving linear mathematical problems. Linear algebraic equations. Linear ordinary and partial differential equations using theoretical/numerical analysis based on linear operator theory. prereq: Chemical engineering grad student or instr consent
MATS 8201 - Applied Math
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Integrated approach to solving linear mathematical problems. Linear algebraic equations. Linear ordinary and partial differential equations using theoretical/numerical analysis based on linear operator theory. prereq: Materials science grad student or instructor consent.
CHEN 5751 - Biochemical Engineering
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Chemical engineering principles applied to analysis/design of complex cellular/enzyme processes. Quantitative framework for design of cells for production of proteins, synthesis of antibodies with mammalian cells, or degradation of toxic compounds in contaminated soil. prereq: [3005 or 4005], [concurrent registration is required (or allowed) in 3006 or concurrent registration is required (or allowed) in 4006], [concurrent registration is required (or allowed) in 3102 or concurrent registration is required (or allowed) in 4102]
CHEN 5753 - Advanced Biomedical Transport Processes
Credits: 3.0 [max 3.0]
Course Equivalencies: BMEn 5311/ChEn 5753/ME 5381
Grading Basis: A-F or Aud
Typically offered: Every Spring
Fluid, mass, heat transport in biological systems. Mass transfer across membranes, fluid flow in capillaries, interstitium, veins, and arteries Heat transfer in single cells/tissues. Whole organ, body heat transfer issues. Blood flow, oxygenation. Heat/mass transfer in respiratory systems. Biotransport issues in artificial organs, membrane oxygenators, drug delivery applications. prereq: 3005 or 4005 or equiv
CHEN 8101 - Fluid Mechanics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Equations of change of mass, momentum, angular momentum. Kinematics of deformation, convective transport. Applications to fluid statics/dynamics of Newtonian fluids. Examples of exact solutions of Navier-Stokes equations, useful simplifications. prereq: Chemical engineering grad student or instr consent
CHEN 8102 - Introduction to Rheology
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This course will describe flow behavior of complex fluids from both a macroscopic experimental point of view, in which we focus on characterizing nontrivial responses to stress and strain, and from a microstructural point of view, in which we focus on the microstructural and molecular origins of observed behavior. Primary topics will include: ? Linear viscoelasticity (dynamic response to varied types of small deformation) ? Non-linear phenomena (non-Newtonian flow and nonlinear elasticity) ? Phenomenological constitutive relations ? Experimental methods for shear and extensional flow ? Microstructural models of colloidal dispersions and polymer liquids
CHEN 8104 - Coating Process Fundamentals
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring & Summer
Process functions. Viscous flow and rheology of polymer solutions and particulate suspensions. Capillarity, wetting. Electrostatic effects. Phase change, colloidal transformations, mass/heat transfer in drying. Kinetics in curing. Stress and property development in solidifying polymeric coatings. Illustrations drawn from theoretical modeling, flow visualization, and stopped-process microscopy. prereq: Chemical engineering grad major or instr consent
CHEN 8401 - Physical and Chemical Thermodynamics
Credits: 3.0 [max 3.0]
Course Equivalencies: ChEn 4706/ChEn 8401
Grading Basis: A-F or Aud
Typically offered: Every Fall
Principles of thermodynamics with emphasis on solving problems encountered in chemical engineering and materials science. An organized exposition of fundamental concepts that will help students understand and analyze the systems they are likely to encounter while conducting original research. This course is for students who seek a much deeper understanding than a typical undergraduate course provides. prereq: Undergraduate engineering course or chemistry course in thermodynamics, Chemical Engineering graduate student, or instructor consent.
CHEN 8402 - Statistical Thermodynamics and Kinetics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Introduction to statistical mechanical description of equilibrium and non-equilibrium properties of matter. Emphasizes fluids, classical statistical mechanics. prereq: Chemical engineering grad student or instr consent
CHEN 8501 - Chemical Rate Processes: Analysis of Chemical Reactors
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Design of reactors for heat management and with catalytic processes. Steady state and transient behavior. Polymerization, combustion, solids processing, and environmental modeling. Design of multiphase reactors. prereq: [Course in chemical reactor engineering, chemical engineering grad student] 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
MATS 5517 - Microscopy of Materials
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
A basic introduction to electron microscopy (EM) methods and techniques for materials characterization. The course is intended for junior- and senior-level undergraduates and graduate students interested in obtaining a basic understanding of characterization with EM. Topics to be covered include an introduction to instrumentation, basics of scattering theory, and a survey of imaging, diffraction, and analytical measurement techniques. Current and emerging techniques will also be covered, including machine learning and big data for EM and time-resolved measurements. Students will research a specific topic of interest over the course of the semester, culminating in a project paper and a class presentation.
MATS 8002 - Thermodynamics and Kinetics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
First three laws of thermodynamics, free energy, equilibrium constants, fugacity and activity relationships, solution models, order-disorder transitions, phase transitions. Elementary statistical mechanics. Applications to materials systems, including surface energies, multicomponent equilibria, reaction kinetics, mass transport, diffusion.
MATS 8003 - Electronic Properties
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Basic physical theory of bonding in metals, alloys, and semiconductors. Review of modern physics, statistical physics, and solid state physics. Structure of matter emphasizing electronic processes. Techniques for predicting and understanding electronic structure of solids. Transport theory, elementary theory of magnetism, and superconductivity. prereq: instr consent
MATS 8004 - Mechanical Properties
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Defects in crystalline materials, including point defects, dislocations, and grain boundaries. Structure and movement of defects related to mechanical behavior of materials. Tools used to understand crystals and crystallography.
MATS 8217 - Transmission Electron Microscopy
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This course is an introduction to transmission electron microscopy (TEM) and materials characterization using TEM. Topics include description and operation of TEMs, electron sources, basics of electron optics, interaction of electrons with specimen, diffraction, imaging techniques, and microanalysis. The goal of this course is to enable you to understand the fundamentals of TEM and microanalysis, read the scientific literature and determine which TEM-based method would be best to solve the problem you encounter in your own research. In a process you will learn about instrumentation, structure of materials, diffraction physics, optics, and condensed matter physics.
CHEN 5771 - Colloids and Dispersions
Credits: 3.0 [max 3.0]
Course Equivalencies: ChEn 5771/MatS 5771
Grading Basis: A-F or Aud
Typically offered: Every Fall
Preparation, stability, coagulation kinetics or colloidal solutions. DLVO theory, electrokinetic phenomena. Properties of micelles, other microstructures. prereq: Physical chemistry
MATS 5771 - Colloids and Dispersions
Credits: 3.0 [max 3.0]
Course Equivalencies: inactive
Grading Basis: A-F or Aud
Typically offered: Every Fall
Preparation, stability, coagulation kinetics, or colloidal solutions. DLVO theory, electrokinetic phenomena. Properties of micelles, other microstructures. prereq: Physical chemistry
CHEN 5803 - Chemical and Materials Technology Commercialization
Credits: 3.0 [max 3.0]
Course Equivalencies: MatS 5803 / ChEn 5803
Grading Basis: A-F only
Typically offered: Every Fall
Introduction to chemical and materials technology commercialization including a focus on products, markets, customers, and processes for brining innovations to market. Pre-requisite courses: CHEN 3101 or MATS 3001.
MATS 5803 - Chemical and Materials Technology Commercialization
Credits: 3.0 [max 3.0]
Course Equivalencies: MatS 5803 / ChEn 5803
Grading Basis: A-F only
Typically offered: Every Fall
Introduction to chemical and materials technology commercialization including a focus on products, markets, customers, and processes for brining innovations to market.
CHEN 8001 - Structure and Symmetry of Materials
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Comprehensive description of structure of materials, including metals, semiconductors, organic crystals, polymers, and liquid crystals. Atomic and molecular ordering, influence of intermolecular forces on symmetry and structure. Principles of scattering and use of X-ray, neutron, and electron diffraction.
MATS 8001 - Structure and Symmetry of Materials
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Comprehensive description of structure of materials, including metals, semiconductors, organic crystals, polymers, and liquid crystals. Atomic and molecular ordering, influence of intermolecular forces on symmetry and structure. Principles of scattering and use of X-ray, neutron, and electron diffraction. prereq: MatS and ChEn majors must take this course for a grade
CHEN 8221 - Synthetic Polymer Chemistry
Credits: 4.0 [max 4.0]
Course Equivalencies: ChEn 8221/MatS 8221/Chem 8221
Grading Basis: A-F or Aud
Typically offered: Every Fall
Condensation, radical, ionic, emulsion, ring-opening, metal-catalyzed polymerizations. Chain conformation, solution thermodynamics, molecular weight characterization, physical properties. prereq: [Undergrad organic chemistry course, undergrad physical chemistry course] or instr consent
MATS 8221 - Synthetic Polymer Chemistry
Credits: 4.0 [max 4.0]
Course Equivalencies: ChEn 8221/MatS 8221/Chem 8221
Grading Basis: A-F or Aud
Typically offered: Every Fall
Condensation, radical, ionic, emulsion, ring-opening, metal-catalyzed polymerizations. Chain conformation, solution thermodynamics, molecular weight characterization, physical properties. prereq: [Undergrad organic chemistry course, undergrad physical chemistry course] or instr consent
CHEN 8301 - Physical Rate Processes I: Transport
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Survey of mass transfer, dilute, and concentrated diffusion. Brownian motion. Diffusion coefficients in polymers, of electrolytes, and at critical points. Multicomponent diffusion. Mass transfer correlations/predictions. Mass transfer coupled with chemical reaction.
MATS 8301 - Physical Rate Processes I: Transport
Credits: 3.0 [max 3.0]
Course Equivalencies: ChEn 8301/MatS 8301
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Survey of mass transfer, dilute, and concentrated diffusion. Brownian motion. Diffusion coefficients in polymers, of electrolytes, and at critical points. Multicomponent diffusion. Mass transfer correlations/predictions. Mass transfer coupled with chemical reaction.
CSCI 5103 - Operating Systems
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Conceptual foundation of operating system designs and implementations. Relationships between operating system structures and machine architectures. UNIX implementation mechanisms as examples. prereq: 4061 or instr consent
CSCI 5105 - Introduction to Distributed Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Distributed system design and implementation. Distributed communication and synchronization, data replication and consistency, distributed file systems, fault tolerance, and distributed scheduling. prereq: [5103 or equiv] or instr consent
CSCI 5211 - Data Communications and Computer Networks
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4211/CSci 5211/INET 4002
Typically offered: Every Fall
Concepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, network layer/routing protocols, transport, congestion/flow control, emerging high-speed networks, network programming interfaces, networked applications. Case studies using Ethernet, Token Ring, FDDI, TCP/IP, ATM, Email, HTTP, and WWW. prereq: [4061 or instr consent], basic knowledge of [computer architecture, operating systems, probability], grad student
CSCI 5271 - Introduction to Computer Security
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Concepts of computer, network, and information security. Risk analysis, authentication, access control, security evaluation, audit trails, cryptography, network/database/application security, viruses, firewalls. prereq: 4061 or 5103 or equiv 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 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 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 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 5551 - Introduction to Intelligent Robotic Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Transformations, kinematics/inverse kinematics, dynamics, control. Sensing (robot vision, force control, tactile sensing), applications of sensor-based robot control, robot programming, mobile robotics, microrobotics. prereq: 2031 or 2033 or instr 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 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 5751 - Big Data Engineering and Architecture
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Big data and data-intensive application management, design and processing concepts. Data modeling on different NoSQL databases: key/value, column-family, document, graph-based stores. Stream and real-time processing. Big data architectures. Distributed computing using Spark, Hadoop or other distributed systems. Big data projects. prereq: 4041, 5707, or instructor consent.
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 5802 - Software Engineering II
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Introduction to software testing, software maturity models, cost specification models, bug estimation, software reliability models, software complexity, quality control, and experience report. Student groups specify, design, implement, and test partial software systems. Application of general software development methods and principles from 5801. prereq: 5801 or instr consent
CSCI 8102 - Foundations of Distributed Computing
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Fundamental principles underlying design of distributed and multiprocessor operating systems. Foundations of distributed computing systems; shared multiprocessor systems. prereq: 8101 or instr consent
CSCI 8314 - Sparse Matrix Computations
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Sparsity and sparse matrices. Data structures for sparse matrices. Direct methods for sparse linear systems. Reordering techniques to reduce fill-in such as minimal degree ordering and nested dissection ordering. Iterative methods. Preconditioning algorithms. Algorithms for sparse eigenvalue problems and sparse least-squares. prereq: 5304 or numerical linear algebra course or instr consent
CSCI 8581 - Big Data in Astrophysics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast/Stat/CSci 8581/Phys 8581
Grading Basis: A-F only
Typically offered: Every Spring
This course will introduce key concepts and techniques used to work with large datasets, in the context of the field of astrophysics. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent. Suggested: familiarity with astrophysics topics such as star formation and evolution, galaxies and clusters, composition and expansion of the universe, gravitational wave sources and waveforms, and high-energy astrophysics.
CSCI 8701 - Overview of Database Research
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Research papers from journals and conferences on current topics in databases, such as database research methodologies, relational implementation techniques, active databases, storage systems, benchmarking, distributed and parallel databases, new data models, prototype systems, data mining, and future directions. prereq: 5708 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 8735 - Advanced Database Systems
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Database systems for emerging applications, nontraditional query processors, multi-dimensional data indexing. Current research trends. prereq: 4707 or 5707 or 5708
CSCI 8801 - Advanced Software Engineering
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Software reusability, internet/intranet programming, software reengineering, and software safety. prereq: 5801 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 5251 - Optimal Filtering and Estimation
Credits: 3.0 [max 3.0]
Course Equivalencies: AEM 5451/EE 5251
Typically offered: Every Fall
Basic probability theory, stochastic processes. Gauss-Markov model. Batch/recursive least squares estimation. Filtering of linear/nonlinear systems. Continuous-time Kalman-Bucy filter. Unscented Kalman filter, particle filters. Applications. prereq: [[[MATH 2243, STAT 3021] or equiv], CSE grad student] or dept consent; 3025, 4231 recommended
EE 5271 - Robot Vision
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Modern visual perception for robotics that includes position and orientation, camera model and calibration, feature detection, multiple images, pose estimation, vision-based control, convolutional neural networks, reinforcement learning, deep Q-network, and visuomotor policy learning. [Math 2373 or equivalent; EE 1301 or equivalent basic programming course]
EE 5351 - Applied Parallel Programming
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Parallel programming/architecture. Application development for many-core processors. Computational thinking, types of parallelism, programming models, mapping computations effectively to parallel hardware, efficient data structures, paradigms for efficient parallel algorithms, application case studies. prereq: [4363 or equivalent], programming experience (C/C++ preferred)
EE 5355 - Algorithmic Techniques for Scalable Many-core Computing
Credits: 3.0 [max 3.0]
Typically offered: Spring Odd Year
Algorithm techniques for enhancing the scalability of parallel software: scatter-to-gather, problem decomposition, binning, privatization, tiling, regularization, compaction, double-buffering, and data layout. These techniques address the most challenging problems in building scalable parallel software: limited parallelism, data contention, insufficient memory bandwidth, load balance, and communication latency. Programming assignments will be given to reinforce the understanding of the techniques. prereq: basic knowledge of CUDA, experience working in a Unix environment, and experience developing and running scientific codes written in C or C++. Completion of EE 5351 is not required but highly recommended.
EE 5371 - Computer Systems Performance Measurement and Evaluation
Credits: 3.0 [max 3.0]
Course Equivalencies: EE 5371/5863
Typically offered: Periodic Fall & Spring
Tools/techniques for analyzing computer hardware, software, system performance. Benchmark programs, measurement tools, performance metrics. Deterministic/probabilistic simulation techniques, random number generation/testing. Bottleneck analysis. prereq: [4363 or 5361 or CSci 4203 or 5201], [CSE grad student] or dept consent
EE 5389 - Introduction to Predictive Learning
Credits: 3.0 [max 3.0]
Course Equivalencies: EE 4389W/EE 5389
Typically offered: Fall Even 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: EE 3025, STAT 3022 or equivalent; computer programming or MATLAB or similar environment is recommended.
EE 5501 - Digital Communication
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Theory/techniques of modern digital communications. Communication limits. Modulation/detection. Data transmission over channels with intersymbol interference. Optimal/suboptimal sequence detection. Equalization. Error correction coding. Trellis-coded modulation. Multiple access. prereq: [3025, 4501, CSE grad student] or dept consent
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 5542 - Adaptive Digital Signal Processing
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Design, application, and implementation of optimum/adaptive discrete-time FIR/IIR filters. Wiener, Kalman, and Least-Squares. Linear prediction. Lattice structure. LMS, RLS, and Levinson-Durbin algorithms. Channel equalization, system identification, biomedical/sensor array processing, spectrum estimation. Noise cancellation applications. prereq: [4541, 5531, 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 5581 - Information Theory and Coding
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Source/channel models, codes for sources/channels. Entropy, mutual information, capacity, rate-distortion functions. Coding theorems. prereq: [5531, CSE grad student] or dept consent
EE 5585 - Data Compression
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Source coding in digital communications and recording. Codes for lossless compression. Universal lossless codes. Lossless image compression. Scalar and vector quantizer design. Loss source coding theory. Differential coding, trellis codes, transform/subband coding. Analysis/synthesis schemes. prereq: CSE grad student or dept 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
EE 8551 - Multirate Signal Processing and Applications
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Multirate discrete-time systems with applications in modern signal and data processing problems. Hilbert Spaces and Linear Operators; Reisz Bases and Frames; Vector Space Representation of Sampling, Interpolation, Time-frequency analysis and wavelets; Filterbanks and Polyphase Structures; Sparsity and redundancy with applications in linear and nonlinear approximation, super-resolution, blind-source separation. prereq: [CSE grad student] or dept consent
EE 5571 - Statistical Learning and Inference
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Deterministic and random approaches to learning and inference from data, with applications to statistical models for estimation, detection, and classification. Algorithms and their performance include minimum-variance unbiased estimators, sufficient statistics, fundamental bounds, (non)linear least-squares, maximum-likelihood, expectation-maximization, nonparametric density estimators, mean-square error and Bayesian estimators, importance sampling, Kalman and particle filtering, sequential probability ratio test, bootstrap, Monte Carlo Markov Chains, and graphical models. prereq: courses in Stochastic Processes (EE 5531) and Digital Signal Processing (EE 4541)
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
IE 5531 - Engineering Optimization I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Linear programming, simplex method, duality theory, sensitivity analysis, interior point methods, integer programming, branch/bound/dynamic programming. Emphasizes applications in production/logistics, including resource allocation, transportation, facility location, networks/flows, scheduling, production planning. prereq: Upper div or grad student or CNR
IE 5561 - Analytics and Data-Driven Decision Making
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Hands-on experience with modern methods for analytics and data-driven decision making. Methodologies such as linear and integer optimization and supervised and unsupervised learning will be brought together to address problems in a variety of areas such as healthcare, agriculture, sports, energy, and finance. Students will learn how to manipulate data, build and solve models, and interpret and visualize results using a high-level, dynamic programming language. Prerequisites: IE 3521 or equivalent; IE 3011 or IE 5531 or equivalent; proficiency with a programming language such as R, Python, or C.
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.
IE 8531 - Discrete Optimization
Credits: 4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Topics in integer programming and combinatorial optimization. Formulation of models, branch-and-bound. Cutting plane and branch-and-cut algorithms. Polyhedral combinatorics. Heuristic approaches. Introduction to computational complexity.
MSBA 6321 - Data Management, Databases, and Data Warehousing
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Fundamentals of database modeling/design, normalization. Extract, transform, load. Data cubes/setting up data warehouse. Data pre-processing, quality, integration/stewardship issues. Advances in database/storage technologies.
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.
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 7407 - Analysis of Categorical Data
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Contingency tables, odds ratio, relative risk, chi-square tests, log-linear models, logistic regression, conditional logistic regression, Poisson regression, matching, generalized linear models for independent data. SAS/S-Plus used throughout. prereq: 7405, [Stat 5102 or concurrent registration is required (or allowed) in Stat 5102 or Stat 8102 or concurrent registration is required (or allowed) in Stat 8102]
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 7460 - Advanced Statistical Computing
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Statistical computing using SAS, Splus, and FORTRAN or C. Use of pseudo-random number generators, distribution functions. Matrix manipulations with applications to regression and estimation of variance. Simulation studies, minimization of functions, nonlinear regression, macro programming, numerical methods of integration. prereq: [7405, biostatistics major, [C or FORTRAN]] or instr consent
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
PUBH 7485 - Methods for Causal Inference
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Although most of statistical inference focuses on associational relationships among variables, in many biomedical and health sciences contexts the focus is on establishing the causal effect of an intervention or treatment. Drawing causal conclusions can be challenging, particularly in the context of observational data, as treatment assignment may be confounded. The first part of this course focuses on methods to establish the causal effect of a point exposure, i.e., situations in which treatment is given at a single point in time. Methods to estimate causal treatment effects will include outcome regression, propensity score methods (i.e., inverse weighting, matching), and doubly robust approaches. The second half of the course focuses on estimating the effect of a series of treatment decisions during the course of a chronic disease such as cancer, substance abuse, mental health disorders, etc. Methods to estimate these time-varying treatments include marginal structural models estimated by inverse probability weighting, structural nested models estimated by G-estimation, and the (parametric) G-computation algorithm. We will then turn our attention to estimating the optimal treatment sequence for a given subject, i.e., how to determine “the right treatment, for the right patient, at the right time,” using dynamic marginal structural models and methods derived from reinforcement learning (e.g., Q-learning, A-learning) and classification problems (outcome weighted learning, C-learning). PubH 8485 is appropriate for Ph.D students in Biostatistics and Statistics. The homework and projects will focus more on the theoretical aspects of the methods to prepare students for methodological research in this area. PubH 7485 is appropriate for Masters students in Biostatistics and PhD students in other fields who wish to learn causal methods to apply them to topics in the health sciences. This course uses the statistical software of R, a freely available statistical software package, to implement many of the methods we discuss. However, most of the methods discussed in this course can be implemented in any statistical software (e.g., SAS, Stata, SPSS, etc.) and students will be free to use any software for homework assignments. prereq: Background in regression (e.g. linear, logistic, models) at the level of PubH 7405-7406, PubH 6450-6451, PubH 7402, or equiv. Background in statistical theory (Stat 5101-5102 or PubH 7401) is helpful.
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
STAT 5052 - Statistical and Machine Learning
Credits: 3.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Periodic Fall & Spring
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 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 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 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 8112 - Mathematical Statistics II
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Statistical inference, estimation, and hypothesis testing. Convergence and relationship between convergence modes. Asymptotics of maximum likelihood estimators, distribution functions, quantiles. Delta method. prereq: 8111
AST 5731 - Bayesian Astrostatistics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 5731/Stat 5731
Grading Basis: A-F only
Typically offered: Every Fall
This course will introduce Bayesian methods for interpreting and analyzing large data sets from astrophysical experiments. These methods will be demonstrated using astrophysics real-world data sets and a focus on modern statistical software, such as R and python. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent Suggested: statistical course at the level of AST 4031, AST 5031, STAT 3021, or STAT 5021
STAT 5731 - Bayesian Astrostatistics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 5731/Stat 5731
Grading Basis: A-F only
Typically offered: Every Fall
This course will introduce Bayesian methods for interpreting and analyzing large data sets from astrophysical experiments. These methods will be demonstrated using astrophysics real-world data sets and a focus on modern statistical software, such as R and python. prereq: MATH 2263 and MATH 2243, or equivalent; or instructor consent Suggested: statistical course at the level of AST 4031, AST 5031, STAT 3021, or STAT 5021
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
EE 8367 - 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: 5364 or CSci 5204
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.
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)
PUBH 8475 - Statistical Learning and Data Mining
Credits: 3.0 [max 3.0]
Course Equivalencies: 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 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).
CHEN 8993 - Directed Study
Credits: 1.0 -12.0 [max 12.0]
Typically offered: Every Fall, Spring & Summer
CHEN 8994 - Directed Research
Credits: 1.0 -12.0 [max 12.0]
Typically offered: Every Fall, Spring & Summer
MATS 8993 - Directed Study
Credits: 1.0 -12.0 [max 12.0]
Typically offered: Every Fall, Spring & Summer
MATS 8994 - Directed Research
Credits: 1.0 -12.0 [max 12.0]
Typically offered: Every Fall, Spring & Summer