Foundations of Data Science

Units: 3

Estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markov theorem. Estimability, analysis of variance and co variance in a unified manner. Practical model-building in linear regression including residual analysis, regression diagnostics, and variable selection. Emphasis on use of the computer to apply methods with data sets. Credit not given for both ST 705 and ST 503. Note: this course will be offered in person [Spring] and online [Summer].

Offered in Spring and Summer

Units: 3

Course covers basic methods for summarizing and describing data, accounting for variability in data, and techniques for inference. Topics include basic exploratory data analysis, probability distributions, confidence intervals, hypothesis testing, and regression analysis. This is a calculus-based course. Statistical software is used; however, there is no lab associated with the course. Credit not given for this course and ST 511 or ST 513 or ST 515. This course does NOT count as an elective towards a degree or a minor in Statistics. Note: the course will be offered in person [Fall] and online [Fall and Summer].

Offered in Fall and Summer

Units: 3

Vector spaces, linear transformations and matrices, orthogonality, orthogonal transformations with emphasis on rotations and reflections, matrix norms, projectors, least squares, generalized inverses, definite matrices, singular values.

Offered in Fall and Spring

Units: 3

Introduction to uncertainty quantification for physical and biological models. Parameter selection techniques, Bayesian model calibration, propagation of uncertainties, surrogate model construction, local and global sensitivity analysis.

Offered in Fall and Spring

YEAR: Offered Alternate Even Years

Units: 3

Convex optimization methods and their applications in various areas of data science including, but not limited to, signal and image processing, inverse problems, statistical data analysis, machine learning and classification. Basic theory, algorithm design and concrete applications.

Offered in Fall Only

Units: 3

Algorithm design techniques: use of data structures, divide and conquer, dynamic programming, greedy techniques, local and global search. Complexity and analysis of algorithms: asymptotic analysis, worst case and average case, recurrences, lower bounds, NP-completeness. Algorithms for classical problems including sorting, searching and graph problems [connectivity, shortest paths, minimum spanning trees].

Offered in Fall Spring Summer

Units: 3

Advanced database concepts. Logical organization of databases: the entity-relationship model; the relational data model and its languages. Functional dependencies and normal forms. Design, implementation, and optimization of query languages; security and integrity, consurrency control, transaction processing, and distributed database systems.

Offered in Fall and Spring

Units: 3

This course will introduce common statistical learning methods for supervised and unsupervised predictive learning in both the regression and classification settings. Topics covered will include linear and polynomial regression, logistic regression and discriminant analysis, cross-validation and the bootstrap, model selection and regularization methods, splines and generalized additive models, principal components, hierarchical clustering, nearest neighbor, kernel, and tree-based methods, ensemble methods, boosting, and support-vector machines.

Offered in Summer

Units: 3

This course provides an introduction to concepts and methods for extracting knowledge or other useful forms of information from data. This activity, also known under names including data mining, knowledge discovery, and exploratory data analysis, plays an important role in modern science, engineering, medicine, business, and government. Students will apply supervised and unsupervised automated learning methods to extract patterns, make predictions and identify groups from data. Students will also learn about the overall process of data collection and analysis that provides the setting for knowledge discovery, and concomitant issues of privacy and security. Examples and projects introduce the students to application areas including electronic commerce, information security, biology, and medicine. Students cannot get credit for both CSC 422 and CSC 522.

Offered in Fall and Spring

Units: 3

Introduction to and overview of artificial intelligence. Study of AI programming language such as LISP or PROLOG. Elements of AI problem-solving technique. State spaces and search techniques. Logic, theorem proving and associative databases. Introduction to knowledge representation, expert systems and selected topics including natural language processing, vision and robotics.

Offered in Fall and Spring

Units: 3

This course provides an introduction to concepts and methods for extracting knowledge or other useful forms of information from data. This activity, also known under names including data mining, knowledge discovery, and exploratory data analysis, plays an important role in modern science, engineering, medicine, business, and government. Students will apply supervised and unsupervised automated learning methods to extract patterns, make predictions and identify groups from data. Students will also learn about the overall process of data collection and analysis that provides the setting for knowledge discovery, and concomitant issues of privacy and security. Examples and projects introduce the students to application areas including electronic commerce, information security, biology, and medicine. Students cannot get credit for both CSC 422 and CSC 522.

Offered in Fall and Spring

Units: 3

Techniques for the design of neural networks for machine learning. An introduction to deep learning. Emphasis on theoretical and practical aspects including implementations using state-of-the-art software libraries. Requirement: Programming experience [an object-oriented language such as Python], linear algebra [MA 405 or equivalent], and basic probability and statistics.

Offered in Fall and Spring

Units: 3

This course surveys the field of social computing, introducing its key concepts, paradigms, and techniques. Specific topics are selected from the following list: social media and social network analytics, sociological underpinnings, crowdsourcing and surveys, human computation, social mobilization, human decision making, voting theory, judgment aggregation, prediction markets, economic mechanisms, incentives, organizational modeling, argumentation, contracts, norms, mobility and social context, sociotechnical systems, and software engineering with and for social computing. This course incorporates ideas from diverse disciplines [including sociology, psychology, law, economics, political science, logic, statistics, philosophy, business] to provide essential background for future computer science careers in industry and research.

Offered in Fall Only

Units: 3

Algorithm behavior and applicability. Effect of roundoff errors, systems of linear equations and direct methods, least squares via Givens and Householder transformations, stationary and Krylov iterative methods, the conjugate gradient and GMRES methods, convergence of method.

Offered in Fall Only

Units: 3

In this course we will examine Artificial Intelligence [AI] techniques that are used in the design of computer games. We will look at techniques for game playing as well as the design of AI opponents tasked with creating "good experiences" for players. The course will begin with a discussion of general AI, common algorithms, data structures, and representations. From there, we will cover topics in character movement, pathfinding, decision making, strategy, tactics, and learning. In a sequence of programming assignments students will create increasingly sophisticated AI implementations. Students will also critically review the projects conducted by graduate students enrolled in CSC584. CSC majors only. Students cannot get credit for both CSC 484 and CSC 584.

Offered in Spring Only

TERM: Offered in Spring Only

Units: 3

A broad range of advanced topics in machine learning, the building of computer-based systems that can adapt to their environment and learn from their own experience. Theory of learnability, technical details of various learning methods, combination of multiple methods, evaluation of methods, and related topics [at the discretion of the instructor].

Offered in Spring Only

YEAR: Offered Alternate Odd Years

Units: 3

Vector spaces, linear transformations and matrices, orthogonality, orthogonal transformations with emphasis on rotations and reflections, matrix norms, projectors, least squares, generalized inverses, definite matrices, singular values.

Offered in Fall and Spring

Units: 3

Introduction to uncertainty quantification for physical and biological models. Parameter selection techniques, Bayesian model calibration, propagation of uncertainties, surrogate model construction, local and global sensitivity analysis.

Offered in Fall and Spring

YEAR: Offered Alternate Even Years

Units: 3

Convex optimization methods and their applications in various areas of data science including, but not limited to, signal and image processing, inverse problems, statistical data analysis, machine learning and classification. Basic theory, algorithm design and concrete applications.

Offered in Fall Only

Units: 3

Algorithm behavior and applicability. Effect of roundoff errors, systems of linear equations and direct methods, least squares via Givens and Householder transformations, stationary and Krylov iterative methods, the conjugate gradient and GMRES methods, convergence of method.

Offered in Fall and Spring

Units: 3

Computational methods for inverse problems that are governed by partial differential equations. Topics will include variational formulations, ill-posedness, regularization, discretization methods, and optimization algorithms, statistical formulations, and numerical implementations.

Offered in Spring Only

Units: 3

The course provides a graduate-level introduction to the numerical methods of solving linear and nonlinear optimization problems and nonlinear equations, along with the fundamental mathematical theory necessary to develop these algorithms. Topics selected from: Newton's method and Quasi-Newton methods for nonlinear equations and optimization problems, globally convergent extensions, methods for sparse problems, applications to differential equations, integral equations and general minimization problems, methods appropriate for boundary value problems, conic programming, first-order methods for large-scale optimization problems.

Offered in Spring Only

Units: 3

Introduction to statistical models and methods for analyzing various types of spatially referenced data. The focus is on applications with real data and their analysis with statistical programs such as R and SAS. Students are required to write, modify, and run computer code in order to complete homework assignments and final projects.

Offered in Spring Only

Units: 3

Statistical models and methods for the analysis of time series data using both time domain and frequency domain approaches. An introduction and review of necessary statistical concepts will be given, and a statistical computing package will be introduced. Analyses of real data sets using statistical software will be emphasized.

Offered in Fall Only

Units: 3

An introduction to use of statistical methods for analyzing multivariate and longitudinal data collected in experiments and surveys. Topics covered include multivariate analysis of variance, discriminant analysis, principal components analysis, factor analysis, covariance modeling, and mixed effects models such as growth curves and random coefficient models. Emphasis is on use of a computer to perform statistical analysis of multivariate and longitudinal data.

Offered in Spring Only

Units: 3

Introduction to Bayesian concepts of statistical inference; Bayesian learning; Markov chain Monte Carlo methods using existing software [SAS and OpenBUGS]; linear and hierarchical models; model selection and diagnostics.

Offered in Spring Only

Units: 3

Course discusses current big data management practices and software along with statistical paradigms important for big data and predictive analytics. Literate programming and good programming practices are covered.

Offered in Spring Only

Units: 3

Methods for reading, manipulating, and combining data sources including databases. Custom functions, visualizations, and summaries. Common analyses done by data scientists. Methods for communicating results including dashboards. Regular access to a computer for homework and class exercises is required.

Offered in Fall and Summer

Units: 3

This course will introduce common statistical learning methods for supervised and unsupervised predictive learning in both the regression and classification settings. Topics covered will include linear and polynomial regression, logistic regression and discriminant analysis, cross-validation and the bootstrap, model selection and regularization methods, splines and generalized additive models, principal components, hierarchical clustering, nearest neighbor, kernel, and tree-based methods, ensemble methods, boosting, and support-vector machines.

Offered in Summer