Program Details : University Catalogs : University of Minnesota

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

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

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 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).

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 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

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 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 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 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

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)

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 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 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

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)

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

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 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 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 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 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 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 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 5541 - Natural Language Processing

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

Computers are poor conversationalists, despite decades of attempts to change that fact. This course will provide an overview of the computational techniques developed in the attempt to enable computers to interpret and respond appropriately to ideas expressed using natural languages (such as English or French) as opposed to formal languages (such as C++ or Python). Topics in this course will include parsing, semantic analysis, machine translation, dialogue systems, and statistical methods in speech recognition. Suggested prerequisite: CSCI 2041

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 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 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 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 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 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 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 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)

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 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.

IE 8535 - Introduction to Network Science

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

Topics include deterministic and random networks, network flows, matching, game theory, distributed decision making in networks, cooperation in networks, cascades in networks, wisdom of crowds, applications in voting, prediction markets, consumer behavior modeling, revenue management, inventory control and finance. This course is offered to graduate students. Undergraduate students must get permission from the instructor for registering. Prerequisites include probability and optimization (5531 and 8532) but students who have taken similar courses or have the mathematical background can register by instructor permission.

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

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 7445 - Statistics for Human Genetics and Molecular Biology

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

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

PUBH 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 7461 - Exploring and Visualizing Data in R

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

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

PUBH 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

PUBH 8445 - Statistics for Human Genetics and Molecular Biology

Credits:	3.0 [max 3.0]
Course Equivalencies:	PubH 7445/PubH 8445
Typically offered:	Fall Odd Year

Introduction to statistical problems arising in molecular biology. Problems in physical mapping (radiation hybrid mapping, DDP), genetic mapping (pedigree analysis, lod scores, TDT), biopolymer sequence analysis (alignment, motif recognition), and micro array analysis. prereq: [[[Stat 8101, Stat 8102] or equiv], PhD student] or instr consent; some background with molecular biology desirable

PUBH 8446 - Advanced Statistical Genetics and Genomics

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

Genetic mapping of complex traits in humans, modern population genetics with an emphasis on inference based observed molecular genetics data, association studies; statistical methods for low/high level analysis of genomic/proteomic data. Multiple comparison and gene network modeling. prereq: [7445, statistical theory at level of STAT 5101-2; college-level molecular genetics course is recommended] or instr consent

PUBH 8472 - Spatial Biostatistics

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

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

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 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 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 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 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

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Data Science Postbaccalaureate Certificate