{"id":2867,"date":"2022-08-15T14:08:06","date_gmt":"2022-08-15T18:08:06","guid":{"rendered":"https:\/\/www.bu.edu\/cds-faculty\/?page_id=2867"},"modified":"2025-11-06T10:17:08","modified_gmt":"2025-11-06T15:17:08","slug":"phd-course-list","status":"publish","type":"page","link":"https:\/\/www.bu.edu\/cds-faculty\/programs-admissions\/phd-degree\/phd-course-list\/","title":{"rendered":"PhD Course List"},"content":{"rendered":"<p><span>Boston University\u2019s PhD in Computing &amp; Data Sciences offers a rigorous, interdisciplinary curriculum designed to prepare scholars for impactful research and leadership in data-driven fields. Housed within BU\u2019s dynamic Faculty of Computing &amp; Data Sciences, the program provides access to world-class faculty, cutting-edge facilities, and collaborative opportunities that span disciplines\u2014from AI and systems engineering to public health and policy. Students gain both theoretical depth and applied experience, positioning them to drive innovation in academia, industry, and beyond.<\/span><\/p>\n<p>Below are the pre-approved courses that fulfill competencies as of Fall 2023. The course list is revised annually. Students may petition for alternative means to satisfy the competencies using other courses not on the list.\u00a0 <span>Note: The categorizations on this website may be out of date (though the requirements are accurate). Please refer to the\u00a0<\/span><a data-auth=\"NotApplicable\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/docs.google.com\/spreadsheets\/d\/1eWiuo8wz3yYV15pju371gTzCuACGeeKT\/edit?gid=948331267#gid=948331267\" data-linkindex=\"0\" title=\"https:\/\/docs.google.com\/spreadsheets\/d\/1eWiuo8wz3yYV15pju371gTzCuACGeeKT\/edit?gid=948331267#gid=948331267\">Official Approved Methodology Competency Document<\/a><span>.<\/span><\/p>\n<h2>Methodology Core<\/h2>\n<p>Students are required to take at least five courses and must fulfill at least six of the eight competencies listed below. Note that a single course may satisfy multiple competencies.<\/p>\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Mathematical Foundations<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">531<\/span><\/span> Advanced Optimization Algorithms<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CAS MA 123 & 124, or equivalent and CAS CS 132 or equivalent; or conse nt of instructor. - Optimization algorithms, highlighting the fruitful interactions between discrete and continuous. Intended audience is advanced master students and doctoral students. Topics include gradient descent algorithms, online optimization, linear and semidefinite programming, duality, network optimization, submodular optimization, approximation algorithms via continuous relaxations.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">538<\/span><\/span> Fundamentals of Cryptography<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS131 & CASCS237 & CASCS357) or consent of instructor. - Graduate Prerequisites: (CASCS332) - Basic Algorithms to guarantee confidentiality and authenticity of data. Definitions and proofs of security for practical constructions. Topics include perfectly secure encryption, pseudorandom generators, RSA and Elgamal encryption, Diffie-Hellman key agreement, RSA signatures, secret sharing, block and stream ciphers. <\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">576<\/span><\/span> Generalized Linear Models<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: (CASMA 575) or consent of instructor. - Covers topics in linear models beyond MA 575: generalized linear models, analysis of binary and polytomous data, log-linear models, multivariate response models, non-linear models, graphical models, and relevant model selection techniques. Additional topics in modern regression as time allows.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">582<\/span><\/span> Mathematical Statistics<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: (CASMA 581 or ENGEK 381 or ENGEK 500) or consent of instructor. - Point estimation including unbiasedness, efficiency, consistency, sufficiency, minimum variance unbiased estimator, Rao-Blackwell theorem, and Rao-Cramer inequality. Maximum likelihood and method of moment estimations; interval estimation; tests of hypothesis, uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, and chi-square test.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">588<\/span><\/span> Nonparametric Statistics<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CASMA 582 or consent of instructor. - The theory and logic in the development of nonparametric techniques including order statistics, tests based on runs, goodness of fit, rank-order (for location and scale), measures of association, analysis of variance, asymptotic relative efficiency.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CDS<\/span> <span class=\"cf-course-dept\">DS<\/span> <span class=\"cf-course-number\">574<\/span><\/span> Algorithmic Game Theory<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">This course is an introduction to the interdisciplinary area of Algorithmic Mechanism Design: where computational perspectives are applied to economic problems, and economic techniques are brought to problems from computer science. We will explore a broad range of topics at the frontier of new research, starting with some of the fundamentals, such as welfare-maximizing auctions and types of Nash Equilibria. \n\nThroughout the semester, the class will also learn about prevalent topics such as (1) Data Science & Incentives, (2) Mechanism Design for Social Good, and (3) optimization and robustness in mechanism design.  As part of this course, students will engage in a (guided) research project, experiencing the various parts of conducting original research. This course is designed as an introductory graduate - level course but is open to advanced undergraduates with permission from the instructor. \n\nWhile the formal undergraduate prerequisites are DS 120, DS 121, and DS122 and DS 320 (or equivalent), the course assumes strong proficiency in these topics for graduate students. Students should have: - Mathematical maturity and comfort with formal proofs - A solid understanding of probability (discrete and continuous random variables, moments, and conditional probability) - Familiarity with algorithms and computational efficiency. \nUndergraduate students interested in this course should contact Professor Goldner (goldner@bu.edu) before registering for the course.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">674<\/span><\/span> Optimization Theory 2<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">This course is an introduction to optimization problems and algorithms emphasizing problem formulation, basic methodologies and the underlying mathematical structures. We will cover the classical theory as well as the state of the art. The major topics we will cover are: 1. Theory and algorithms for linear programming. 2. Introduction to combinatorial problems and methods for handling intractable problems. 3. Introduction to nonlinear programming. 4. Introduction to network optimization. Optimization techniques have many applications in science and engineering. To name a few: * Optimal routing in communication networks. * Transmission scheduling and resource allocation in sensor networks. * Production planning and scheduling in manufacturing systems. * Fleet management. * Air traffic flow management by airlines. * Optimal resource allocation in manufacturing and communication systems. * Optimal portfolio selection. * Analysis and optimization of fluxes in metabolic networks. * Protein docking. Prerequisites: Working knowledge of Linear Algebra and some degree of mathematical maturity. Same as ENG EC 674, ENG SE 524, ENG EC 674. Students may not receive credits for both.<\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Statistical Modeling &amp; Inference<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">542<\/span><\/span> Principles of Machine Learning<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS365) - Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">570<\/span><\/span> Stochastic Methods of Operations Research<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: (CASMA 225 or CASMA 230 or CDSDS 122) and (CASMA 242 or CASMA 442 or ENGEK 103 or CASCS 132) or consent of instructor. - Poisson processes, Markov chains, queuing theory. Matrix differential equations, differential-difference equations, probability-generating functions, single- and multiple-channel queues, steady-state and transient distributions.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">575<\/span><\/span> Linear Models<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        <div class=\"cf-hub-ind\">  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"cf-hub-head\" alt=\"BU Hub\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-buhub\">BU Hub<\/span>  <\/a>  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"hub-head\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-questionmark\">Learn More<\/span>  <\/a>  <ul class=\"cf-hub-offerings\"><li class=\"cf-hub-area-H\">Quantitative Reasoning II<\/li><li class=\"cf-hub-area-3\">Teamwork\/Collaboration<\/li>  <\/ul><\/div>\n\t\t<p class=\"cf-course-description\">Prerequisites: (CASMA 214 or CASMA 116) and (CASMA 581 or ENGEK 381 or ENGEK 500 or CASCS 237) and (CASMA 242 or CASMA 442 or ENGEK 103 or CDSDS 121 or CASCS 132) or consent of instructor. - Post-introductory course on linear models. Topics to be covered include simple and multiple linear regression, regression with polynomials or factors, analysis of variance, weighted and generalized least squares, transformations, regression diagnostics, variable selection, and extensions of linear models. Effective Fall 2019, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning 2, Teamwork\/Collaboration.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">578<\/span><\/span> Bayesian Statistics<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: CASMA 575 or consent of instructor. - The principles and methods of Bayesian statistics. Subjective probability, Bayes rule, posterior distributions, predictive distributions. Computationally based inference using Monte Carlo integration, Markov chain simulation. Hierarchical models, mixture models, model checking, and methods for Bayesian model selection.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">582<\/span><\/span> Mathematical Statistics<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: (CASMA 581 or ENGEK 381 or ENGEK 500) or consent of instructor. - Point estimation including unbiasedness, efficiency, consistency, sufficiency, minimum variance unbiased estimator, Rao-Blackwell theorem, and Rao-Cramer inequality. Maximum likelihood and method of moment estimations; interval estimation; tests of hypothesis, uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, and chi-square test.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">585<\/span><\/span> Time Series and Forecasting<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: (CASMA 581 or ENGEK 381 or ENGEK 500) or consent of instructor. - Autocorrelation and partial autocorrelation functions; stationary and nonstationary processes; ARIMA and Seasonal ARIMA model identification, estimation, diagnostics, and forecasting. Modeling financial data via ARCH and GARCH models. Volatility estimation; additional topics, including long-range dependence and state-space models.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">588<\/span><\/span> Nonparametric Statistics<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CASMA 582 or consent of instructor. - The theory and logic in the development of nonparametric techniques including order statistics, tests based on runs, goodness of fit, rank-order (for location and scale), measures of association, analysis of variance, asymptotic relative efficiency.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">505<\/span><\/span> Stochastic Processes<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (ENGEC401 & CASMA142) or equivalent and either ENGEK381 or ENGEK500. - Introduction to discrete and continuous-time random processes. Correlation and power spectral density functions. Linear systems driven by random processes. Optimum detection and estimation. Bayesian, Weiner, and Kalman filtering. <\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">719<\/span><\/span> Statistical Learning Theory<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (ENGEC 414 OR ENGEC 503); Undergraduate Corequisites: (ENGEC 505). - Classical and contemporary theories of machine learning. Topics\/emphasis may change based on instructor preference in different years. A project involving computer implementation of a learning or inference algorithm accompanied by or in support of theoretical analysis is required<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">QST<\/span> <span class=\"cf-course-dept\">DS<\/span> <span class=\"cf-course-number\">925<\/span><\/span> Methods for Causal Inference in Strategy Research<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">(Formerly SI 915) This course reviews tools and methods for drawing causal inferences from non-experimental data. The class emphasizes conceptual difficulties associated with establishing causality in observational settings, the strengths and weaknesses of statistical methods based on so-called natural experiments, and the practical problems that arise in the application of these tools. This course is designed to complement a traditional two-semester graduate sequence in econometrics.<\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Efficient &amp; Scalable Algorithms<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">530<\/span><\/span> Advanced Algorithms<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS330) or consent of instructor. - Graduate Prerequisites: (CASCS330) or consent of instructor. - Studies the design and efficiency of algorithms in several areas of computer science. Topics are chosen from graph algorithms, sorting and searching, NP-complete problems, pattern matching, parallel algorithms, and dynamic programming. <\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">565<\/span><\/span> Algorithmic Data Mining<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS 112 & CASCS 330 & CASCS 365). - Introduction to data mining concepts and techniques. Topics include association and correlation discovery, classification and clustering of large datasets, outlier detection. Emphasis on the algorithmic aspects as well as the application of mining in real-world problems.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">539<\/span><\/span> Methods of Scientific Computing<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: (CASMA 225 or CASMA 230) and (CASMA 242 or CASMA 442) and programming experience or consent of instructor. - An introduction to topics including computational linear algebra, solutions of linear equations, numerical integration and solution of differential equations, finite element methods, and methods of stochastic simulation (i.e., Monte Carlo methods).<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CDS<\/span> <span class=\"cf-course-dept\">DS<\/span> <span class=\"cf-course-number\">563<\/span><\/span> Algorithmic Techniques for Taming Big Data<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        <div class=\"cf-hub-ind\">  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"cf-hub-head\" alt=\"BU Hub\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-buhub\">BU Hub<\/span>  <\/a>  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"hub-head\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-questionmark\">Learn More<\/span>  <\/a>  <ul class=\"cf-hub-offerings\"><li class=\"cf-hub-area-4\">Creativity\/Innovation<\/li><li class=\"cf-hub-area-H\">Quantitative Reasoning II<\/li>  <\/ul><\/div>\n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CDSDS110 OR CASCS111 OR ENGEK125 OR equivalent; CDSDS320 OR CASCS330 O R ENGEC330 OR equivalent; CDSDS121 OR CASCS132 OR CASMA242 OR equivale nt; CASMA115 OR CASCS327 OR ENGEK381 OR equivalent, OR conse - Growing amounts of available data lead to significant challenges in processing them efficiently. In many cases, it is no longer possible to design feasible algorithms that can freely access the entire data set. Instead of that we often have to resort to techniques that allow for reducing the amount of data such as sampling, sketching, dimensionality reduction, and core sets. Apart from these approaches, the course will also explore scenarios in which large data sets are distributed across several machines or even geographical locations and the goal is to design efficient communication protocols or MapReduce algorithms. The course will include a final project and programming assignments in which we will explore the performance of our techniques when applied to publicly available data sets. <\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Predictive Analysis &amp; ML<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">507<\/span><\/span> Networks and Markets: Theory and Applications<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: For CS students: CASCS 112, CS 131, a probability or statistics course equivalent to CS 237 or MA 214 or CDSDS 122 is required. CS330 is recommended as co-requisite. For DS students: CDSDS 210, DS 122 is required. DS 320 is recommended as co-requisite. Successful digital platforms build on both network science and market design. This course explores the interplay between agents, algorithms, and data in networked settings like matching markets and ad auctions. Digital markets will be studied in depth through independent projects.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">542<\/span><\/span> Principles of Machine Learning<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS365) - Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">575<\/span><\/span> Linear Models<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        <div class=\"cf-hub-ind\">  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"cf-hub-head\" alt=\"BU Hub\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-buhub\">BU Hub<\/span>  <\/a>  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"hub-head\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-questionmark\">Learn More<\/span>  <\/a>  <ul class=\"cf-hub-offerings\"><li class=\"cf-hub-area-H\">Quantitative Reasoning II<\/li><li class=\"cf-hub-area-3\">Teamwork\/Collaboration<\/li>  <\/ul><\/div>\n\t\t<p class=\"cf-course-description\">Prerequisites: (CASMA 214 or CASMA 116) and (CASMA 581 or ENGEK 381 or ENGEK 500 or CASCS 237) and (CASMA 242 or CASMA 442 or ENGEK 103 or CDSDS 121 or CASCS 132) or consent of instructor. - Post-introductory course on linear models. Topics to be covered include simple and multiple linear regression, regression with polynomials or factors, analysis of variance, weighted and generalized least squares, transformations, regression diagnostics, variable selection, and extensions of linear models. Effective Fall 2019, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning 2, Teamwork\/Collaboration.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">576<\/span><\/span> Generalized Linear Models<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: (CASMA 575) or consent of instructor. - Covers topics in linear models beyond MA 575: generalized linear models, analysis of binary and polytomous data, log-linear models, multivariate response models, non-linear models, graphical models, and relevant model selection techniques. Additional topics in modern regression as time allows.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">503<\/span><\/span> Introduction to Learning from Data<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: EK381 or equivalent; EK102 or equivalent; MA225 or equivalent; EK125 or equivalent.\n\nThis is an introductory graduate course in (classical) machine learning covering the basic principles and methods of four major non-sequential supervised and unsupervised learning problems namely, classification, regression, clustering, and dimensionality reduction. A variety of contemporary applications will be explored through homeworks and a project.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">523<\/span><\/span> Deep Learning<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: A strong mathematical background in calculus, linear algebra, and prob ability & statistics, as well as prior coursework in machine learning and programming experience in Python. Mathematical and machine learning background for deep learning.  Feed-forward networks. Backpropagation. Training strategies for deep networks. Convolutional networks. Recurrent neural networks. Transformers. Diffusion Models. Deep unsupervised learning. Exposure to Pytorch and other modern programming tools. Other recent topics, time permitting. Same as CAS CS 523. Students may not receive credit for both.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">719<\/span><\/span> Statistical Learning Theory<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (ENGEC 414 OR ENGEC 503); Undergraduate Corequisites: (ENGEC 505). - Classical and contemporary theories of machine learning. Topics\/emphasis may change based on instructor preference in different years. A project involving computer implementation of a learning or inference algorithm accompanied by or in support of theoretical analysis is required<\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Optimization Algorithms<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">507<\/span><\/span> Networks and Markets: Theory and Applications<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: For CS students: CASCS 112, CS 131, a probability or statistics course equivalent to CS 237 or MA 214 or CDSDS 122 is required. CS330 is recommended as co-requisite. For DS students: CDSDS 210, DS 122 is required. DS 320 is recommended as co-requisite. Successful digital platforms build on both network science and market design. This course explores the interplay between agents, algorithms, and data in networked settings like matching markets and ad auctions. Digital markets will be studied in depth through independent projects.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">531<\/span><\/span> Advanced Optimization Algorithms<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CAS MA 123 & 124, or equivalent and CAS CS 132 or equivalent; or conse nt of instructor. - Optimization algorithms, highlighting the fruitful interactions between discrete and continuous. Intended audience is advanced master students and doctoral students. Topics include gradient descent algorithms, online optimization, linear and semidefinite programming, duality, network optimization, submodular optimization, approximation algorithms via continuous relaxations.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">569<\/span><\/span> Optimization Methods of Operations Research<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: (CASMA 225 or CASMA 230 or CDSDS 122) and (CASMA 242 or CASMA 442 or ENGEK 103 or CDSCS 132) or consent of instructor. - Optimization of linear functions: linear programming, simplex method; transportation, assignment, and network problems. Optimization of non-linear functions: unconstrained optima, constrained optima and Lagrange multipliers, Kuhn-Tucker conditions, calculus of variations, and Euler's equation.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CDS<\/span> <span class=\"cf-course-dept\">DS<\/span> <span class=\"cf-course-number\">574<\/span><\/span> Algorithmic Game Theory<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">This course is an introduction to the interdisciplinary area of Algorithmic Mechanism Design: where computational perspectives are applied to economic problems, and economic techniques are brought to problems from computer science. We will explore a broad range of topics at the frontier of new research, starting with some of the fundamentals, such as welfare-maximizing auctions and types of Nash Equilibria. \n\nThroughout the semester, the class will also learn about prevalent topics such as (1) Data Science & Incentives, (2) Mechanism Design for Social Good, and (3) optimization and robustness in mechanism design.  As part of this course, students will engage in a (guided) research project, experiencing the various parts of conducting original research. This course is designed as an introductory graduate - level course but is open to advanced undergraduates with permission from the instructor. \n\nWhile the formal undergraduate prerequisites are DS 120, DS 121, and DS122 and DS 320 (or equivalent), the course assumes strong proficiency in these topics for graduate students. Students should have: - Mathematical maturity and comfort with formal proofs - A solid understanding of probability (discrete and continuous random variables, moments, and conditional probability) - Familiarity with algorithms and computational efficiency. \nUndergraduate students interested in this course should contact Professor Goldner (goldner@bu.edu) before registering for the course.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">524<\/span><\/span> Optimization Theory and Methods<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (ENGEK103 OR CASMA142) - Introduction to optimization problems and algorithms emphasizing problem formulation, basic methodologies, and underlying mathematical structures. Classical optimization theory as well as recent advances in the field. Topics include modeling issues and formulations, simplex method, duality theory, sensitivity analysis, large-scale optimization, integer programming, interior-point methods, non-linear programming optimality conditions, gradient methods, and conjugate direction methods. Applications are considered; case studies included. Extensive paradigms from production planning and scheduling in manufacturing systems. Other illustrative applications include fleet management, air traffic flow management, optimal routing in communication networks, and optimal portfolio selection. Same as ENG EC 674, ENG SE 524, ENG SE 674. Students may not receive credit for both. <\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">674<\/span><\/span> Optimization Theory 2<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">This course is an introduction to optimization problems and algorithms emphasizing problem formulation, basic methodologies and the underlying mathematical structures. We will cover the classical theory as well as the state of the art. The major topics we will cover are: 1. Theory and algorithms for linear programming. 2. Introduction to combinatorial problems and methods for handling intractable problems. 3. Introduction to nonlinear programming. 4. Introduction to network optimization. Optimization techniques have many applications in science and engineering. To name a few: * Optimal routing in communication networks. * Transmission scheduling and resource allocation in sensor networks. * Production planning and scheduling in manufacturing systems. * Fleet management. * Air traffic flow management by airlines. * Optimal resource allocation in manufacturing and communication systems. * Optimal portfolio selection. * Analysis and optimization of fluxes in metabolic networks. * Protein docking. Prerequisites: Working knowledge of Linear Algebra and some degree of mathematical maturity. Same as ENG EC 674, ENG SE 524, ENG EC 674. Students may not receive credits for both.<\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Computational Complexity<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">530<\/span><\/span> Advanced Algorithms<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS330) or consent of instructor. - Graduate Prerequisites: (CASCS330) or consent of instructor. - Studies the design and efficiency of algorithms in several areas of computer science. Topics are chosen from graph algorithms, sorting and searching, NP-complete problems, pattern matching, parallel algorithms, and dynamic programming. <\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">531<\/span><\/span> Advanced Optimization Algorithms<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CAS MA 123 & 124, or equivalent and CAS CS 132 or equivalent; or conse nt of instructor. - Optimization algorithms, highlighting the fruitful interactions between discrete and continuous. Intended audience is advanced master students and doctoral students. Topics include gradient descent algorithms, online optimization, linear and semidefinite programming, duality, network optimization, submodular optimization, approximation algorithms via continuous relaxations.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">535<\/span><\/span> Complexity Theory<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: Prerequisites: CS330. - CASCS 332 is recommended. Covers topics of current interest in the theory of computation chosen from computational models, games and hierarchies of problems, abstract complexity theory, informational complexity theory, time-space trade-offs, probabilistic computation, and recent work on particular combinatorial problems.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">537<\/span><\/span> Randomness in Computing<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS 330 OR CASCS 530). CASCS 535 is recommended or consent of instructor. - Graduate Prerequisites: CASCS 330 or CASCS 530 is recommended. - Survey of probabilistic ideas of the theory of computation. Topics may include Monte Carlo and Las Vegas probabilistic computations; average case complexity and analysis; random and pseudorandom strings; games and cryptographic protocol; information; inductive inference; reliability; others. (Offered alternate years.)<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">538<\/span><\/span> Fundamentals of Cryptography<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS131 & CASCS237 & CASCS357) or consent of instructor. - Graduate Prerequisites: (CASCS332) - Basic Algorithms to guarantee confidentiality and authenticity of data. Definitions and proofs of security for practical constructions. Topics include perfectly secure encryption, pseudorandom generators, RSA and Elgamal encryption, Diffie-Hellman key agreement, RSA signatures, secret sharing, block and stream ciphers. <\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CDS<\/span> <span class=\"cf-course-dept\">DS<\/span> <span class=\"cf-course-number\">563<\/span><\/span> Algorithmic Techniques for Taming Big Data<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        <div class=\"cf-hub-ind\">  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"cf-hub-head\" alt=\"BU Hub\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-buhub\">BU Hub<\/span>  <\/a>  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"hub-head\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-questionmark\">Learn More<\/span>  <\/a>  <ul class=\"cf-hub-offerings\"><li class=\"cf-hub-area-4\">Creativity\/Innovation<\/li><li class=\"cf-hub-area-H\">Quantitative Reasoning II<\/li>  <\/ul><\/div>\n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CDSDS110 OR CASCS111 OR ENGEK125 OR equivalent; CDSDS320 OR CASCS330 O R ENGEC330 OR equivalent; CDSDS121 OR CASCS132 OR CASMA242 OR equivale nt; CASMA115 OR CASCS327 OR ENGEK381 OR equivalent, OR conse - Growing amounts of available data lead to significant challenges in processing them efficiently. In many cases, it is no longer possible to design feasible algorithms that can freely access the entire data set. Instead of that we often have to resort to techniques that allow for reducing the amount of data such as sampling, sketching, dimensionality reduction, and core sets. Apart from these approaches, the course will also explore scenarios in which large data sets are distributed across several machines or even geographical locations and the goal is to design efficient communication protocols or MapReduce algorithms. The course will include a final project and programming assignments in which we will explore the performance of our techniques when applied to publicly available data sets. <\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Programming &amp; Software Design<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">561<\/span><\/span> Data Systems Architectures<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        <div class=\"cf-hub-ind\">  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"cf-hub-head\" alt=\"BU Hub\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-buhub\">BU Hub<\/span>  <\/a>  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"hub-head\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-questionmark\">Learn More<\/span>  <\/a>  <ul class=\"cf-hub-offerings\"><li class=\"cf-hub-area-N\">Oral and\/or Signed Communication<\/li><li class=\"cf-hub-area-2\">Research and Information Literacy<\/li>  <\/ul><\/div>\n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CAS CS 210 or equivalent and CAS CS 460\/660. - Discusses the design of data systems that can address the modern challenges of managing and accessing large, ever-growing, diverse sets of data, often streaming from heterogenous sources, in the context of continuously evolving hardware and software. We use examples from several data management areas including relational systems, distributed database systems, key value stores, newSQL and NoSQL systems, data systems for machine learning (and machine learning for data systems), interactive analytics, and data management as a service. Effective Spring 2021, this course fulfills a single unit in each of the following BU Hub areas: Oral and\/or Signed Communication, Research and Information Literacy.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">523<\/span><\/span> Deep Learning<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: A strong mathematical background in calculus, linear algebra, and prob ability & statistics, as well as prior coursework in machine learning and programming experience in Python. Mathematical and machine learning background for deep learning.  Feed-forward networks. Backpropagation. Training strategies for deep networks. Convolutional networks. Recurrent neural networks. Transformers. Diffusion Models. Deep unsupervised learning. Exposure to Pytorch and other modern programming tools. Other recent topics, time permitting. Same as CAS CS 523. Students may not receive credit for both.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">527<\/span><\/span> High Performance Programming with Multicore and GPUs<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: EC413 or equivalent; programming in C\n\nConsiders theory and practice of hardware-aware programming. Key theme is obtaining a significant fraction of potential performance through knowledge of the underlying computing platform and how the platform interacts with programs. Studies architecture of, and programming methods for, contemporary high-performance processors. These include complex processor cores, multicore processors, and graphics processors. Laboratory component includes use and evaluation of programming methods on these processors through applications such as matrix operations and the Fast Fourier Transform.<\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Large-scale Data Management<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">561<\/span><\/span> Data Systems Architectures<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        <div class=\"cf-hub-ind\">  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"cf-hub-head\" alt=\"BU Hub\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-buhub\">BU Hub<\/span>  <\/a>  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"hub-head\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-questionmark\">Learn More<\/span>  <\/a>  <ul class=\"cf-hub-offerings\"><li class=\"cf-hub-area-N\">Oral and\/or Signed Communication<\/li><li class=\"cf-hub-area-2\">Research and Information Literacy<\/li>  <\/ul><\/div>\n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CAS CS 210 or equivalent and CAS CS 460\/660. - Discusses the design of data systems that can address the modern challenges of managing and accessing large, ever-growing, diverse sets of data, often streaming from heterogenous sources, in the context of continuously evolving hardware and software. We use examples from several data management areas including relational systems, distributed database systems, key value stores, newSQL and NoSQL systems, data systems for machine learning (and machine learning for data systems), interactive analytics, and data management as a service. Effective Spring 2021, this course fulfills a single unit in each of the following BU Hub areas: Oral and\/or Signed Communication, Research and Information Literacy.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">562<\/span><\/span> Advanced Database Applications<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS460) or consent of instructor. - Research issues in the design and implementation of modern database systems. Spatial, temporal, and spatiotemporal index structures. Indexing methods for image and multimedia databases and data warehouses. New data analysis techniques for large databases, clustering and rule discovery for very large datasets. <\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">565<\/span><\/span> Algorithmic Data Mining<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS 112 & CASCS 330 & CASCS 365). - Introduction to data mining concepts and techniques. Topics include association and correlation discovery, classification and clustering of large datasets, outlier detection. Emphasis on the algorithmic aspects as well as the application of mining in real-world problems.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CDS<\/span> <span class=\"cf-course-dept\">DS<\/span> <span class=\"cf-course-number\">563<\/span><\/span> Algorithmic Techniques for Taming Big Data<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        <div class=\"cf-hub-ind\">  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"cf-hub-head\" alt=\"BU Hub\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-buhub\">BU Hub<\/span>  <\/a>  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"hub-head\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-questionmark\">Learn More<\/span>  <\/a>  <ul class=\"cf-hub-offerings\"><li class=\"cf-hub-area-4\">Creativity\/Innovation<\/li><li class=\"cf-hub-area-H\">Quantitative Reasoning II<\/li>  <\/ul><\/div>\n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CDSDS110 OR CASCS111 OR ENGEK125 OR equivalent; CDSDS320 OR CASCS330 O R ENGEC330 OR equivalent; CDSDS121 OR CASCS132 OR CASMA242 OR equivale nt; CASMA115 OR CASCS327 OR ENGEK381 OR equivalent, OR conse - Growing amounts of available data lead to significant challenges in processing them efficiently. In many cases, it is no longer possible to design feasible algorithms that can freely access the entire data set. Instead of that we often have to resort to techniques that allow for reducing the amount of data such as sampling, sketching, dimensionality reduction, and core sets. Apart from these approaches, the course will also explore scenarios in which large data sets are distributed across several machines or even geographical locations and the goal is to design efficient communication protocols or MapReduce algorithms. The course will include a final project and programming assignments in which we will explore the performance of our techniques when applied to publicly available data sets. <\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n<h2>Subject Core<\/h2>\n<p>To satisfy the subject core requirement, students must take a minimum of three courses in a single subject area.<\/p>\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Theoretical Foundations<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">530<\/span><\/span> Advanced Algorithms<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS330) or consent of instructor. - Graduate Prerequisites: (CASCS330) or consent of instructor. - Studies the design and efficiency of algorithms in several areas of computer science. Topics are chosen from graph algorithms, sorting and searching, NP-complete problems, pattern matching, parallel algorithms, and dynamic programming. <\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">531<\/span><\/span> Advanced Optimization Algorithms<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CAS MA 123 & 124, or equivalent and CAS CS 132 or equivalent; or conse nt of instructor. - Optimization algorithms, highlighting the fruitful interactions between discrete and continuous. Intended audience is advanced master students and doctoral students. Topics include gradient descent algorithms, online optimization, linear and semidefinite programming, duality, network optimization, submodular optimization, approximation algorithms via continuous relaxations.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">535<\/span><\/span> Complexity Theory<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: Prerequisites: CS330. - CASCS 332 is recommended. Covers topics of current interest in the theory of computation chosen from computational models, games and hierarchies of problems, abstract complexity theory, informational complexity theory, time-space trade-offs, probabilistic computation, and recent work on particular combinatorial problems.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">538<\/span><\/span> Fundamentals of Cryptography<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS131 & CASCS237 & CASCS357) or consent of instructor. - Graduate Prerequisites: (CASCS332) - Basic Algorithms to guarantee confidentiality and authenticity of data. Definitions and proofs of security for practical constructions. Topics include perfectly secure encryption, pseudorandom generators, RSA and Elgamal encryption, Diffie-Hellman key agreement, RSA signatures, secret sharing, block and stream ciphers. <\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">576<\/span><\/span> Generalized Linear Models<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: (CASMA 575) or consent of instructor. - Covers topics in linear models beyond MA 575: generalized linear models, analysis of binary and polytomous data, log-linear models, multivariate response models, non-linear models, graphical models, and relevant model selection techniques. Additional topics in modern regression as time allows.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">582<\/span><\/span> Mathematical Statistics<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: (CASMA 581 or ENGEK 381 or ENGEK 500) or consent of instructor. - Point estimation including unbiasedness, efficiency, consistency, sufficiency, minimum variance unbiased estimator, Rao-Blackwell theorem, and Rao-Cramer inequality. Maximum likelihood and method of moment estimations; interval estimation; tests of hypothesis, uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, and chi-square test.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">MA<\/span> <span class=\"cf-course-number\">588<\/span><\/span> Nonparametric Statistics<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CASMA 582 or consent of instructor. - The theory and logic in the development of nonparametric techniques including order statistics, tests based on runs, goodness of fit, rank-order (for location and scale), measures of association, analysis of variance, asymptotic relative efficiency.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">674<\/span><\/span> Optimization Theory 2<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">This course is an introduction to optimization problems and algorithms emphasizing problem formulation, basic methodologies and the underlying mathematical structures. We will cover the classical theory as well as the state of the art. The major topics we will cover are: 1. Theory and algorithms for linear programming. 2. Introduction to combinatorial problems and methods for handling intractable problems. 3. Introduction to nonlinear programming. 4. Introduction to network optimization. Optimization techniques have many applications in science and engineering. To name a few: * Optimal routing in communication networks. * Transmission scheduling and resource allocation in sensor networks. * Production planning and scheduling in manufacturing systems. * Fleet management. * Air traffic flow management by airlines. * Optimal resource allocation in manufacturing and communication systems. * Optimal portfolio selection. * Analysis and optimization of fluxes in metabolic networks. * Protein docking. Prerequisites: Working knowledge of Linear Algebra and some degree of mathematical maturity. Same as ENG EC 674, ENG SE 524, ENG EC 674. Students may not receive credits for both.<\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Software &amp; Systems<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">561<\/span><\/span> Data Systems Architectures<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        <div class=\"cf-hub-ind\">  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"cf-hub-head\" alt=\"BU Hub\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-buhub\">BU Hub<\/span>  <\/a>  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"hub-head\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-questionmark\">Learn More<\/span>  <\/a>  <ul class=\"cf-hub-offerings\"><li class=\"cf-hub-area-N\">Oral and\/or Signed Communication<\/li><li class=\"cf-hub-area-2\">Research and Information Literacy<\/li>  <\/ul><\/div>\n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CAS CS 210 or equivalent and CAS CS 460\/660. - Discusses the design of data systems that can address the modern challenges of managing and accessing large, ever-growing, diverse sets of data, often streaming from heterogenous sources, in the context of continuously evolving hardware and software. We use examples from several data management areas including relational systems, distributed database systems, key value stores, newSQL and NoSQL systems, data systems for machine learning (and machine learning for data systems), interactive analytics, and data management as a service. Effective Spring 2021, this course fulfills a single unit in each of the following BU Hub areas: Oral and\/or Signed Communication, Research and Information Literacy.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">562<\/span><\/span> Advanced Database Applications<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS460) or consent of instructor. - Research issues in the design and implementation of modern database systems. Spatial, temporal, and spatiotemporal index structures. Indexing methods for image and multimedia databases and data warehouses. New data analysis techniques for large databases, clustering and rule discovery for very large datasets. <\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CDS<\/span> <span class=\"cf-course-dept\">DS<\/span> <span class=\"cf-course-number\">537<\/span><\/span> Data Science for Conservation Decisions<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        <div class=\"cf-hub-ind\">  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"cf-hub-head\" alt=\"BU Hub\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-buhub\">BU Hub<\/span>  <\/a>  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"hub-head\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-questionmark\">Learn More<\/span>  <\/a>  <ul class=\"cf-hub-offerings\"><li class=\"cf-hub-area-O\">Digital\/Multimedia Expression<\/li><li class=\"cf-hub-area-H\">Quantitative Reasoning II<\/li><li class=\"cf-hub-area-2\">Research and Information Literacy<\/li>  <\/ul><\/div>\n\t\t<p class=\"cf-course-description\">This course covers the application of quantitative methods to support conservation decisions. Ecosystem value mapping, systematic conservation planning, policy instrument design, rigorous impact evaluation, decision theory, data visualization. Implementations in state-of-the-art open-source software. Real-life case studies from the U.S. and abroad. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Digital\/Multimedia Expression, Quantitative Reasoning II, Research and Information Literacy.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CDS<\/span> <span class=\"cf-course-dept\">DS<\/span> <span class=\"cf-course-number\">563<\/span><\/span> Algorithmic Techniques for Taming Big Data<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        <div class=\"cf-hub-ind\">  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"cf-hub-head\" alt=\"BU Hub\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-buhub\">BU Hub<\/span>  <\/a>  <a href=\"http:\/\/www.bu.edu\/hub\/what-is-the-hub\/\" target=\"_blank\" class=\"hub-head\">    <span aria-hidden=\"true\" class=\"bu-hub-iconstyles icon-questionmark\">Learn More<\/span>  <\/a>  <ul class=\"cf-hub-offerings\"><li class=\"cf-hub-area-4\">Creativity\/Innovation<\/li><li class=\"cf-hub-area-H\">Quantitative Reasoning II<\/li>  <\/ul><\/div>\n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CDSDS110 OR CASCS111 OR ENGEK125 OR equivalent; CDSDS320 OR CASCS330 O R ENGEC330 OR equivalent; CDSDS121 OR CASCS132 OR CASMA242 OR equivale nt; CASMA115 OR CASCS327 OR ENGEK381 OR equivalent, OR conse - Growing amounts of available data lead to significant challenges in processing them efficiently. In many cases, it is no longer possible to design feasible algorithms that can freely access the entire data set. Instead of that we often have to resort to techniques that allow for reducing the amount of data such as sampling, sketching, dimensionality reduction, and core sets. Apart from these approaches, the course will also explore scenarios in which large data sets are distributed across several machines or even geographical locations and the goal is to design efficient communication protocols or MapReduce algorithms. The course will include a final project and programming assignments in which we will explore the performance of our techniques when applied to publicly available data sets. <\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">527<\/span><\/span> High Performance Programming with Multicore and GPUs<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: EC413 or equivalent; programming in C\n\nConsiders theory and practice of hardware-aware programming. Key theme is obtaining a significant fraction of potential performance through knowledge of the underlying computing platform and how the platform interacts with programs. Studies architecture of, and programming methods for, contemporary high-performance processors. These include complex processor cores, multicore processors, and graphics processors. Laboratory component includes use and evaluation of programming methods on these processors through applications such as matrix operations and the Fast Fourier Transform.<\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Data Mining &amp; Machine Learning<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">542<\/span><\/span> Principles of Machine Learning<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS365) - Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">565<\/span><\/span> Algorithmic Data Mining<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: (CASCS 112 & CASCS 330 & CASCS 365). - Introduction to data mining concepts and techniques. Topics include association and correlation discovery, classification and clustering of large datasets, outlier detection. Emphasis on the algorithmic aspects as well as the application of mining in real-world problems.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">ENG<\/span> <span class=\"cf-course-dept\">EC<\/span> <span class=\"cf-course-number\">523<\/span><\/span> Deep Learning<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Prerequisites: A strong mathematical background in calculus, linear algebra, and prob ability & statistics, as well as prior coursework in machine learning and programming experience in Python. Mathematical and machine learning background for deep learning.  Feed-forward networks. Backpropagation. Training strategies for deep networks. Convolutional networks. Recurrent neural networks. Transformers. Diffusion Models. Deep unsupervised learning. Exposure to Pytorch and other modern programming tools. Other recent topics, time permitting. Same as CAS CS 523. Students may not receive credit for both.<\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">AI and Human-Centered Computing<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CAS<\/span> <span class=\"cf-course-dept\">CS<\/span> <span class=\"cf-course-number\">505<\/span><\/span> Introduction to Natural Language Processing<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\"><\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">Undergraduate Prerequisites: CASCS 365 OR (CASCS 440 and CASMA 225) - Natural language processing (NLP) is a field of AI which aims to equip computers with the ability to intelligently process natural (human) language. This course explores statistical and machine learning techniques for the automatic analysis of natural language data.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CDS<\/span> <span class=\"cf-course-dept\">DS<\/span> <span class=\"cf-course-number\">682<\/span><\/span> Responsible AI, Law, Ethics & Society<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">This course addresses the deployment of Artificial Intelligence systems across various societal domains, raising fundamental challenges and concerns such as accountability, liability, fairness, transparency, and privacy. Tackling these challenges necessitates an interdisciplinary approach, integrating principles and practices from data science, ethics, and law. This unique course will bring together students from computing and data science disciplines as well as law and public policy disciplines from multiple institutions. \n\nPermission is required to register for this course. Course page: https:\/\/learn.responsibly.ai. Please fill out an application form here: https:\/\/forms.gle\/bMRECdYcMUwHj7xG8. Instructor: shlomi@bu.edu.<\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">DS for Social &amp; Behavioral Sciences<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><br \/>\n<div class=\"course-feed\"><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CDS<\/span> <span class=\"cf-course-dept\">DS<\/span> <span class=\"cf-course-number\">574<\/span><\/span> Algorithmic Game Theory<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Fall and Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">This course is an introduction to the interdisciplinary area of Algorithmic Mechanism Design: where computational perspectives are applied to economic problems, and economic techniques are brought to problems from computer science. We will explore a broad range of topics at the frontier of new research, starting with some of the fundamentals, such as welfare-maximizing auctions and types of Nash Equilibria. \n\nThroughout the semester, the class will also learn about prevalent topics such as (1) Data Science & Incentives, (2) Mechanism Design for Social Good, and (3) optimization and robustness in mechanism design.  As part of this course, students will engage in a (guided) research project, experiencing the various parts of conducting original research. This course is designed as an introductory graduate - level course but is open to advanced undergraduates with permission from the instructor. \n\nWhile the formal undergraduate prerequisites are DS 120, DS 121, and DS122 and DS 320 (or equivalent), the course assumes strong proficiency in these topics for graduate students. Students should have: - Mathematical maturity and comfort with formal proofs - A solid understanding of probability (discrete and continuous random variables, moments, and conditional probability) - Familiarity with algorithms and computational efficiency. \nUndergraduate students interested in this course should contact Professor Goldner (goldner@bu.edu) before registering for the course.<\/p>\n\t<\/div>\n\n\t\n<\/aside><aside class=\"cf-course\">\n\t<div class=\"cf-course-card\">\n\t\t<h3 class=\"cf-course-title\"><span class=\"cf-course-id\"><span class=\"cf-course-college\">CDS<\/span> <span class=\"cf-course-dept\">DS<\/span> <span class=\"cf-course-number\">682<\/span><\/span> Responsible AI, Law, Ethics & Society<\/h3>\n\t\t<p class=\"meta cf-course-info\"><span class=\"cf-course-credits\">4 credits.<\/span> <span class=\"cf-course-offered\">Spring<\/span> <span class=\"cf-course-prereqs\"><\/span><\/p>\n        \n\t\t<p class=\"cf-course-description\">This course addresses the deployment of Artificial Intelligence systems across various societal domains, raising fundamental challenges and concerns such as accountability, liability, fairness, transparency, and privacy. Tackling these challenges necessitates an interdisciplinary approach, integrating principles and practices from data science, ethics, and law. This unique course will bring together students from computing and data science disciplines as well as law and public policy disciplines from multiple institutions. \n\nPermission is required to register for this course. Course page: https:\/\/learn.responsibly.ai. Please fill out an application form here: https:\/\/forms.gle\/bMRECdYcMUwHj7xG8. Instructor: shlomi@bu.edu.<\/p>\n\t<\/div>\n\n\t\n<\/aside><\/div><\/p>\n<p><\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Boston University\u2019s PhD in Computing &amp; Data Sciences offers a rigorous, interdisciplinary curriculum designed to prepare scholars for impactful research and leadership in data-driven fields. Housed within BU\u2019s dynamic Faculty of Computing &amp; Data Sciences, the program provides access to world-class faculty, cutting-edge facilities, and collaborative opportunities that span disciplines\u2014from AI and systems engineering to [&hellip;]<\/p>\n","protected":false},"author":14057,"featured_media":0,"parent":616,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/cds-faculty\/wp-json\/wp\/v2\/pages\/2867"}],"collection":[{"href":"https:\/\/www.bu.edu\/cds-faculty\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.bu.edu\/cds-faculty\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cds-faculty\/wp-json\/wp\/v2\/users\/14057"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cds-faculty\/wp-json\/wp\/v2\/comments?post=2867"}],"version-history":[{"count":36,"href":"https:\/\/www.bu.edu\/cds-faculty\/wp-json\/wp\/v2\/pages\/2867\/revisions"}],"predecessor-version":[{"id":18914,"href":"https:\/\/www.bu.edu\/cds-faculty\/wp-json\/wp\/v2\/pages\/2867\/revisions\/18914"}],"up":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cds-faculty\/wp-json\/wp\/v2\/pages\/616"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/cds-faculty\/wp-json\/wp\/v2\/media?parent=2867"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}