Program Details : University Catalogs : University of Minnesota

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 5123 - Recommender Systems

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

An overview of recommender systems, including content-based and collaborative algorithms for recommendation, programming of recommender systems, and evaluation and metrics for recommender systems. prereq: Java programming and 2033 and 3081, or instructor consent.

CSCI 5125 - Collaborative and Social Computing

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

Introduction to computer-supported cooperative work, social computing. Technology, research methods, theory, case studies of group computing systems. Readings, hands-on experience. prereq: 5115 or instr consent

CSCI 5127W - Embodied Computing: Design & Prototyping (WI)

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

In this course, you will learn and apply the principles of embodied computing to human-centered challenges. Through a semester-long team project, you will learn and demonstrate mastery of human-centered embodied computing through two phases: (1) investigating human needs and current embodied practices and (2) rapidly prototyping and iterating embodied computing solutions. One of the ways you will demonstrate this mastery is through the collaborative creation of a written document and project capstone video describing your process and prototype. prereq: CSci 4041, upper division or graduate student, or instructor permission; CSci 5115 or equivalent recommended.

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 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 5471 - Modern Cryptography

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

Introduction to cryptography. Theoretical foundations, practical applications. Threats, attacks, and countermeasures, including cryptosystems and cryptographic protocols. Secure systems/networks. History of cryptography, encryption (conventional, public key), digital signatures, hash functions, message authentication codes, identification, authentication, applications. prereq: [2011, 4041, [familiarity with number theory or finite fields]] 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 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 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 5563 - Multiview 3D Geometry in Computer Vision

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

The 3D spatial relationship between cameras and scenes in computer vision. Application to tasks such as planning robots, reconstructing scenes from photos, and understanding human behaviors from body-worn cameras data. Multiview theory fundamentals, structure-from-motion, state-of-the-art approaches, and current research integration. Prereq: Students enrolling in this course must have completed CSCI 5561 or have instructor consent.

CSCI 5607 - Fundamentals of Computer Graphics 1

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

Fundamental algorithms in computer graphics. Emphasizes programming projects in C/C++. Scan conversion, hidden surface removal, geometrical transformations, projection, illumination/shading, parametric cubic curves, texture mapping, antialising, ray tracing. Developing graphics software, graphics research. prereq: concurrent registration is required (or allowed) in 2033, concurrent registration is required (or allowed) in 3081

CSCI 5608 - Fundamentals of Computer Graphics II

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

Advanced topics in image synthesis, modeling, rendering. Image processing, image warping, global illumination, non-photorealistic rendering, texture synthesis. Parametric cubic surfaces, subdivision surfaces, acceleration techniques, advanced texture mapping. Programming in C/C++. prereq: 5607 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 5611 - Animation & Planning in Games

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

Theory behind algorithms used to bring virtual worlds to life. Computer animation topics. Real-time, interactive techniques used in modern games. Physically-based animation, motion planning, character animation, simulation in virtual worlds. prereq: 4041 or 4611 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 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 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 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 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 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 5221 - Foundations of Advanced Networking

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

Design principles, protocol mechanisms. Network algorithmics, implementation techniques. Advanced network architectures, state-of-art/emerging networking technologies/applications, network modeling. Simulation, experiments. prereq: 4211 or 5211 or equiv; intro course in computer networks recommended

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 5552 - Sensing and Estimation in Robotics

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

Bayesian estimation, maximum likelihood estimation, Kalman filtering, particle filtering. Sensor modeling and fusion. Mobile robot motion estimation (odometry, inertial,laser scan matching, vision-based) and path planning. Map representations, landmark-based localization, Markov localization, simultaneous localization/mapping (SLAM), multi-robot localization/mapping. prereq: [5551, Stat 3021] or instr consent

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 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 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 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 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 5527 - Deep Learning: Models, Computation, and Applications

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

This course introduces the basic ingredients of deep learning, describes effective models and computational principles, and samples important applications. Topics include universal approximation theorems, basics of numerical optimization, auto-differentiation, convolution neural networks, recurrent neural networks, generative neural networks, representation learning, and deep reinforcement learning. Prerequisite: CSCI 5521 or equivalent Maturity in linear algebra, calculus, and basic probability is assumed. Familiarity with Python is necessary to complete the homework assignments and final project.

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 8101 - Advanced Operating Systems

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

Successful research systems and existing theory of systems design. Goal is not merely to catalog systems or learn mathematics, but to develop a sense of elegance of design that leads to successful systems. prereq: 5103 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 8115 - Human-Computer Interaction and User Interface Technology

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

Current research issues in human-computer interaction, user interface toolkits and frameworks, and related areas. Research techniques, model-based development, gesture-based interfaces, constraint-based programming, event processing models, innovative systems, HCI in multimedia systems. prereq: 5115 or instr consent

CSCI 8117 - Understanding the Social Web

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

Research on the social web. Read, present, and discuss papers, do homework using social web research techniques such as data analysis and simulation. Semester research project. prereq: CS grad or instr consent

CSCI 8161 - Advanced Compiler Techniques

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

Techniques for uniprocessors and parallel computers. Fundamental program analysis instruments such as data flow analysis and data dependence analysis. Variety of code generation and transformation techniques. prereq: 4061 or instr consent

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 8211 - Advanced Computer Networks and Their Applications

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

Current research issues in traffic and resource management, quality-of-service provisioning for integrated services networks (such as next-generation Internet and ATM networks) and multimedia networking. prereq: 5211 or instr consent

CSCI 8271 - Security and Privacy in Computing

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

Recent security/privacy issues in computer systems/networks. Threats, attacks, countermeasures. Security research, authentication, network security, wireless security, computer system security, anonymous system, pseudonym, access control, intrusion detection system, cryptographic protocols. How to pursue research in security and design secure systems. prereq: [5211, 5103] or instr consent; 5471 or EE 5248 or Math 5248 or equiv recommended

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 8363 - Numerical Linear Algebra in Data Exploration

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

Computational methods in linear algebra, matrix decompositions for linear equations, least squares, eigenvalue problems, singular value decomposition, conditioning, stability in method for machine learning, large data collections. Principal directions, unsupervised clustering, latent semantic indexing, linear least squares fit. Markov chain models on hyperlink structure. prereq: 5304 or instr consent

CSCI 8442 - Computational Geometry and Applications

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

Designing efficient algorithms and data structures for geometric problems. Models of computation, convex hulls, geometric duality, multidimensional search, Voronoi diagrams and Delauney triangulations, linear programming in fixed dimensions, lower bound techniques. Applications, advanced topics. prereq: 5421 or instr consent

CSCI 8523 - AI for Earth: Monitoring Changes in the Environment via Deep Learning

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

Advances in machine learning in conjunction with massive amounts of data from Earth observing satellites offer huge potential for improving our understanding of how the Earth's environment and ecosystems have been changing and how they are being impacted by humans actions and changing climate. Deep learning approaches, that have had phenomenal success in the domain of computer vision and language/speech translation, hold promise in dealing with environmental problems. However, due to challenges that are unique to environmental applications, off-the-shelf deep learning techniques developed for related applications such as computer vision often have limited utility. This class will introduce to the students the promise and challenges in using deep learning techniques to analyze complex, multi-scale, spatio-temporal data for monitoring changes in the Earth and its environment on a global scale. Prerequisites: CSci 5523, CSci 5521, or equivalent

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 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

CSCI 8980 - Special Advanced Topics in Computer Science

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

Lectures and informal discussions. prereq: instr consent

CSCI 8991 - Independent Study

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

Independent study with professor. prereq: instr consent

CSCI 8994 - Directed Research in Computer Science

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

Directed research with professor. prereq: instr consent

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 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 5117 - Developing the Interactive Web

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

Hands-on design experience using modern web development tools. Students work in teams to develop software programs using each of four toolkits. Analyze developments in forum posts and classroom discussions. prereq: 4131 or 5131 or instr consent; upper div or grad in CSci recommended

CSCI 5123 - Recommender Systems

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

An overview of recommender systems, including content-based and collaborative algorithms for recommendation, programming of recommender systems, and evaluation and metrics for recommender systems. prereq: Java programming and 2033 and 3081, or instructor consent.

CSCI 5125 - Collaborative and Social Computing

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

Introduction to computer-supported cooperative work, social computing. Technology, research methods, theory, case studies of group computing systems. Readings, hands-on experience. prereq: 5115 or instr consent

CSCI 5143 - Real-Time and Embedded Systems

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

Real-time systems that require timely response by computer to external stimulus. Embedded systems in which computer is part of machine. Increasing importance of these systems in commercial products. How to control robots and video game consoles. Lecture, informal lab. prereq: [4061 or instr consent], experience with C language

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 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 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 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 5527 - Deep Learning: Models, Computation, and Applications

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

This course introduces the basic ingredients of deep learning, describes effective models and computational principles, and samples important applications. Topics include universal approximation theorems, basics of numerical optimization, auto-differentiation, convolution neural networks, recurrent neural networks, generative neural networks, representation learning, and deep reinforcement learning. Prerequisite: CSCI 5521 or equivalent Maturity in linear algebra, calculus, and basic probability is assumed. Familiarity with Python is necessary to complete the homework assignments and final project.

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 5552 - Sensing and Estimation in Robotics

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

Bayesian estimation, maximum likelihood estimation, Kalman filtering, particle filtering. Sensor modeling and fusion. Mobile robot motion estimation (odometry, inertial,laser scan matching, vision-based) and path planning. Map representations, landmark-based localization, Markov localization, simultaneous localization/mapping (SLAM), multi-robot localization/mapping. prereq: [5551, Stat 3021] 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 5563 - Multiview 3D Geometry in Computer Vision

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

The 3D spatial relationship between cameras and scenes in computer vision. Application to tasks such as planning robots, reconstructing scenes from photos, and understanding human behaviors from body-worn cameras data. Multiview theory fundamentals, structure-from-motion, state-of-the-art approaches, and current research integration. Prereq: Students enrolling in this course must have completed CSCI 5561 or have instructor consent.

CSCI 5607 - Fundamentals of Computer Graphics 1

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

Fundamental algorithms in computer graphics. Emphasizes programming projects in C/C++. Scan conversion, hidden surface removal, geometrical transformations, projection, illumination/shading, parametric cubic curves, texture mapping, antialising, ray tracing. Developing graphics software, graphics research. prereq: concurrent registration is required (or allowed) in 2033, concurrent registration is required (or allowed) in 3081

CSCI 5608 - Fundamentals of Computer Graphics II

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

Advanced topics in image synthesis, modeling, rendering. Image processing, image warping, global illumination, non-photorealistic rendering, texture synthesis. Parametric cubic surfaces, subdivision surfaces, acceleration techniques, advanced texture mapping. Programming in C/C++. prereq: 5607 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 5611 - Animation & Planning in Games

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

Theory behind algorithms used to bring virtual worlds to life. Computer animation topics. Real-time, interactive techniques used in modern games. Physically-based animation, motion planning, character animation, simulation in virtual worlds. prereq: 4041 or 4611 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 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 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 5991 - Independent Study

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

Independent study arranged with CS faculty member. prereq: instr consent; may be repeated for cr

CSCI 5994 - Directed Research

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

Directed research arranged with faculty member. prereq: instr consent; may be repeated for cr

CSCI 8115 - Human-Computer Interaction and User Interface Technology

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

Current research issues in human-computer interaction, user interface toolkits and frameworks, and related areas. Research techniques, model-based development, gesture-based interfaces, constraint-based programming, event processing models, innovative systems, HCI in multimedia systems. prereq: 5115 or instr consent

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 8211 - Advanced Computer Networks and Their Applications

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

Current research issues in traffic and resource management, quality-of-service provisioning for integrated services networks (such as next-generation Internet and ATM networks) and multimedia networking. prereq: 5211 or instr consent

CSCI 8271 - Security and Privacy in Computing

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

Recent security/privacy issues in computer systems/networks. Threats, attacks, countermeasures. Security research, authentication, network security, wireless security, computer system security, anonymous system, pseudonym, access control, intrusion detection system, cryptographic protocols. How to pursue research in security and design secure systems. prereq: [5211, 5103] or instr consent; 5471 or EE 5248 or Math 5248 or equiv recommended

CSCI 8363 - Numerical Linear Algebra in Data Exploration

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

Computational methods in linear algebra, matrix decompositions for linear equations, least squares, eigenvalue problems, singular value decomposition, conditioning, stability in method for machine learning, large data collections. Principal directions, unsupervised clustering, latent semantic indexing, linear least squares fit. Markov chain models on hyperlink structure. prereq: 5304 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 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 8991 - Independent Study

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

Independent study with professor. prereq: instr consent

CSCI 8994 - Directed Research in Computer Science

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

Directed research with professor. prereq: instr consent

AEM 5401 - Intermediate Dynamics

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

Three-dimensional Newtonian mechanics, kinematics of rigid bodies, dynamics of rigid bodies, generalized coordinates, holonomic constraints, Lagrange equations, applications. prereq: CSE upper div or grad, 2012, Math 2243

AEM 5451 - Optimal Estimation

Credits:	3.0 [max 3.0]
Course Equivalencies:	AEM 5451/EE 5251
Typically offered:	Fall Even Year

Basic probability theory. Batch/recursive least squares estimation. Filtering of linear/non-linear systems using Kalman and extended Kalman filters. Applications to sensor fusion, fault detection, and system identification. prereq: [[MATH 2243 or STAT 3021 or equiv], [4321 or EE 4231 or ME 5281 or equiv]] or instr consent

AEM 8411 - Advanced Dynamics

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

Advanced analytical mechanics and non-linear dynamical systems. Review of Lagrangian mechanics. Hamilton's equations of motion. Canonical transformations and Hamilton-Jacobi theory. Kane's equations. Analysis of differential equations and numerical methods. Phase plane, averaging, and perturbation methods. Stability/bifurcations of equilibria. prereq: 5401 or equiv

AEM 8423 - Convex Optimization Methods in Control

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

Practical aspects of convex optimization methods applied to solve design/analysis problems in control theory. prereq: 5321 or EE 5231 or equiv

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 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 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 8151 - Biomedical Electronics and Implantable Microsystems

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

This class is about bioelectronics and the synergy between electronics and biomedical applications. It discusses how to architect robust ultra-low-power electronics with applications in implantable, noninvasive, wireless, sensing, and stimulating biomedical systems. Half of the classes span feedback systems, transistor device physics, noise, and circuit-analysis techniques to provide a circuit-foundation. The other half are research papers that describe the utilization of these circuits in implantable and wearable systems. Some of these systems include cochlear implants for the deaf, brain implants for the blind and paralyzed, cardiac devices for noninvasive medical monitoring, and biomolecular sensing systems. Prerequisites: BMEn 5101 or equivalent background in bioinstrumentation and electric circuits.

CEGE 8211 - Theory of Traffic Flow

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

Definitions/measurements of basic traffic flow parameters, fundamental relationships. Macroscopic continuum and microscopic traffic flow models. Schockwaves and applications. Flow, speed, headway, and other statistical distributions of traffic parameters. Gap availability/acceptance. Simulation of traffic flow. Traffic control theory, queuing theory, applications.

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 5235 - Robust Control System Design

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

Development of control system design ideas; frequency response techniques in design of single-input/single-output (and MI/MO) systems. Robust control concepts. CAD tools. prereq: CSE grad, 3015, 5231 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 5301 - VLSI Design Automation I

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

Basic graph/numerical algorithms. Algorithms for logic/high-level synthesis. Simulation algorithms at logic/circuit level. Physical-design algorithms. prereq: [2301, CSE grad student] or dept consent

EE 5333 - Analog Integrated Circuit Design

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

Fundamental circuits for analog signal processing. Design issues associated with MOS/BJT devices. Design/testing of circuits. Selected topics (e.g., modeling of basic IC components, design of operational amplifier or comparator or analog sampled-data circuit filter). prereq: [3115, CSE grad student] or dept consent

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 5364 - 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 and I/O systems. Interactions between computer software and hardware. Methodologies of computer design. prereq: [[4363 or CSci 4203], CSE grad student] or dept consent

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 5373 - Data Modeling Using R

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

Introduction to data modeling and the R language programming. Multi-factor linear regression modeling. Residual analysis and model quality evaluation. Response prediction. Training and testing. Integral lab. An introductory course in probability and statistics is suggested but not required; basic programming skills in some high-level programming language, such as C/C++, Java, Fortran, etc also suggested.

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 5505 - Wireless Communication

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

Introduction to wireless communication systems. Propagation modeling, digital communication over fading channels, diversity and spread spectrum techniques, radio mobile cellular systems design, performance evaluation. Current European, North American, and Japanese wireless networks. prereq: [4501, CSE grad student] or dept consent; 5501 recommended

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 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 5653 - Physical Principles of Magnetic Materials

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

Physics of diamagnetism, paramagnetism, ferromagnetism, antiferromagnetism, ferrimagnetism. Ferromagnetic phenomena. Static/dynamic theory of micromagnetics, magneto-optics, and magnetization dynamics. Magnetic material applications. prereq: CSE grad student or dept consent

EE 5741 - Advanced Power Electronics

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

Physics of solid-state power devices, passive components, magnetic optimization, advanced topologies. Unity power factor correction circuits, EMI issues, snubbers, soft switching in dc/ac converters. Practical considerations. Very low voltage output converters. Integrated computer simulations. 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 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

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 5532 - Stochastic Models

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

Introduction to stochastic modeling and stochastic processes. Probability review, random variables, discrete- and continuous-time Markov chains, queueing systems, simulation. Applications to industrial and systems engineering including production and inventory control. prereq: Undergraduate probability and statistics. Familiarity with computer programming in a high level language.

MATH 5075 - Mathematics of Options, Futures, and Derivative Securities I

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

Mathematical background (e.g., partial differential equations, Fourier series, computational methods, Black-Scholes theory, numerical methods--including Monte Carlo simulation). Interest-rate derivative securities, exotic options, risk theory. First course of two-course sequence. prereq: Two yrs calculus, basic computer skills

MATH 5165 - Mathematical Logic I

Credits:	4.0 [max 4.0]
Course Equivalencies:	Math 4152/5165
Typically offered:	Every Fall

Theory of computability: notion of algorithm, Turing machines, primitive recursive functions, recursive functions, Kleene normal form, recursion theorem. Propositional logic. prereq: 2283 or 3283 or Phil 5201 or CSci course in theory of algorithms or instr consent

MATH 5248 - Cryptology and Number Theory

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

Classical cryptosystems. One-time pads, perfect secrecy. Public key ciphers: RSA, discrete log. Euclidean algorithm, finite fields, quadratic reciprocity. Message digest, hash functions. Protocols: key exchange, secret sharing, zero-knowledge proofs. Probablistic algorithms: pseudoprimes, prime factorization. Pseudo-random numbers. Elliptic curves. prereq: 2 sems soph math

MATH 5251 - Error-Correcting Codes, Finite Fields, Algebraic Curves

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

Information theory: channel models, transmission errors. Hamming weight/distance. Linear codes/fields, check bits. Error processing: linear codes, Hamming codes, binary Golay codes. Euclidean algorithm. Finite fields, Bose-Chaudhuri-Hocquenghem codes, polynomial codes, Goppa codes, codes from algebraic curves. prereq: 2 sems soph math

MATH 5335 - Geometry I

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

Advanced two-dimensional Euclidean geometry from a vector viewpoint. Theorems/problems about triangles/circles, isometries, connections with Euclid's axioms. Hyperbolic geometry, how it compares with Euclidean geometry. prereq: [2243 or 2373 or 2573], [concurrent registration is required (or allowed) in 2263 or concurrent registration is required (or allowed) in 2374 or concurrent registration is required (or allowed) in 2574]

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 5447 - Theoretical Neuroscience

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

Nonlinear dynamical system models of neurons and neuronal networks. Computation by excitatory/inhibitory networks. Neural oscillations, adaptation, bursting, synchrony. Memory systems. prereq: 2243 or 2373 or 2574

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 5486 - Introduction To Numerical Methods II

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

Numerical integration/differentiation. Numerical solution of initial-value problems, boundary value problems for ordinary differential equations, partial differential equations. prereq: 5485

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 5654 - Prediction and Filtering

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

Markov chains, Wiener process, stationary sequences, Ornstein-Uhlenbeck process. Partially observable Markov processes (hidden Markov models), stationary processes. Equations for general filters, Kalman filter. Prediction of future values of partially observable processes. prereq: 5651 or Stat 5101

MATH 5705 - Enumerative Combinatorics

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

Basic enumeration, bijections, inclusion-exclusion, recurrence relations, ordinary/exponential generating functions, partitions, Polya theory. Optional topics include trees, asymptotics, listing algorithms, rook theory, involutions, tableaux, permutation statistics. prereq: [2243 or 2373 or 2573], [2263 or 2283 or 2374 or 2574 or 3283]

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 8211 - Commutative and Homological Algebra

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

Selected topics. prereq: 8202 or instr consent

MATH 8212 - Commutative and Homological Algebra

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

Selected topics. prereq: 8211 or instr consent

MATH 8253 - Algebraic Geometry

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

Curves, surfaces, projective space, affine and projective varieties. Rational maps. Blowing-up points. Zariski topology. Irreducible varieties, divisors. prereq: 8202 or instr consent

MATH 8254 - Algebraic Geometry

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

Sheaves, ringed spaces, and schemes. Morphisms. Derived functors and cohomology, Serre duality. Riemann-Roch theorem for curves, Hurwitz's theorem. Surfaces: monoidal transformations, birational transformations. prereq: 8253 or instr consent

MATH 8270 - Topics in Algebraic Geometry

Credits:	1.0 -3.0 [max 12.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Fall & Spring

N/A prereq: Math 8201, Math 8202; offered for one year or one semester as circumstances warrant

MATH 8301 - Manifolds and Topology

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

Classification of compact surfaces, fundamental group/covering spaces. Homology group, basic cohomology. Application to degree of a map, invariance of domain/dimension. prereq: [Some point-set topology, algebra] or instr consent

MATH 8302 - Manifolds and Topology

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

Smooth manifolds, tangent spaces, embedding/immersion, Sard's theorem, Frobenius theorem. Differential forms, integration. Curvature, Gauss-Bonnet theorem. Time permitting: de Rham, duality in manifolds. prereq: 8301 or instr consent

MATH 8306 - Algebraic Topology

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

Singular homology, cohomology theory with coefficients. Eilenberg-Stenrod axioms, Mayer-Vietoris theorem. prereq: 8301 or instr consent

MATH 8307 - Algebraic Topology

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

Basic homotopy theory, cohomology rings with applications. Time permitting: fibre spaces, cohomology operations, extra-ordinary cohomology theories. prereq: 8306 or instr consent

MATH 8365 - Riemannian Geometry

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

Riemannian metrics, curvature. Bianchi identities, Gauss-Bonnet theorem, Meyers's theorem, Cartan-Hadamard theorem. prereq: 8301 or basic point-set topology 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 8501 - Differential Equations and Dynamical Systems I

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

Existence, uniqueness, continuity, and differentiability of solutions. Linear theory and hyperbolicity. Basics of dynamical systems. Local behavior near a fixed point, a periodic orbit, and a homoclinic or heteroclinic orbit. Perturbation theory. prereq: 4xxx ODE or instr consent

MATH 8601 - Real Analysis

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

Set theory/fundamentals. Axiom of choice, measures, measure spaces, Borel/Lebesgue measure, integration, fundamental convergence theorems, Riesz representation. prereq: 5616 or instr consent

MATH 8602 - Real Analysis

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

Radon-Nikodym, Fubini theorems. C(X). Lp spaces (introduction to metric, Banach, Hilbert spaces). Stone-Weierstrass theorem. Basic Fourier analysis. Theory of differentiation. prereq: 8601 or instr consent

MATH 8651 - Theory of Probability Including Measure Theory

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

Probability spaces. Distributions/expectations of random variables. Basic theorems of Lebesque theory. Stochastic independence, sums of independent random variables, random walks, filtrations. Probability, moment generating functions, characteristic functions. Laws of large numbers. prereq: 5616 or instr consent

MATH 8991 - Independent Study

Credits:	1.0 -6.0 [max 24.0]
Typically offered:	Every Fall, Spring & Summer

Individually directed study. prereq: instr consent

ME 5241 - Computer-Aided Engineering

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

Apply computer-aided engineering to mechanical design. Engineering design projects and case studies using computer-aided design and finite element analysis software; design optimization and computer graphical presentation of results. prereq: 3222, CSci 1113 or equiv, CSE upper div or grad

ME 5243 - Advanced Mechanism Design

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

Analytical methods of kinematic, dynamic, and kinetoelastodynamic analysis and synthesis of mechanisms. Computerized design for function, path, and motion generation based on Burmeister theory. prereq: CSE upper div or grad, 3222 or equiv, basic kinematics and dynamics of machines; knowledge of CAD packages such as Pro-E recommended

ME 8253 - Computational Nanomechanics

Credits:	3.0 [max 3.0]
Course Equivalencies:	ME 8253/SCIC 8253
Prerequisites:	CSE grad student
Typically offered:	Every Spring

Fundamentals of mechanical properties in nanometer scale. Role of discrete structure and underlying atomic, molecular, and interfacial forces are illustrated with modern examples. Overview of computational atomistic methods. Lectures, hands-on computing using publicly available or personally developed scientific software packages. prereq: CSE grad student

ME 8390 - Advanced Topics in the Thermal Sciences : Biostabilization in Biomedicine, and Biotechnology

Credits:	1.0 -3.0 [max 18.0]
Grading Basis:	A-F or Aud
Typically offered:	Every Spring

Topics vary according to instructor.

ME 8794 - Mechanical Engineering Research

Credits:	1.0 -4.0 [max 4.0]
Grading Basis:	S-N only
Typically offered:	Every Fall, Spring & Summer

Directed research. prereq: instr consent

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.

MSBA 6421 - Predictive Analytics

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

Fundamentals of predictive modeling and machine learning, assessing the performance of predictive models: logistic regression, decision trees, na�ve Bayesian classifiers, support vector machine, ensemble learning, deep neural network, and their applications in structured and unstructured data.

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 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 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 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 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 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 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 8931 - Advanced Topics in Statistics

Credits:	3.0 [max 12.0]
Typically offered:	Periodic Fall & Spring

Topics vary according to student needs/available staff.

STAT 8932 - Advanced Topics in Statistics

Credits:	3.0 [max 12.0]
Typically offered:	Periodic Fall & Spring

Topics vary according to student needs/available staff.

STAT 8933 - Advanced Topics in Statistics

Credits:	3.0 [max 12.0]
Typically offered:	Every Fall & Spring

Topics vary according to student needs and available staff.

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