Courses

The listing of a course description here does not guarantee a course’s being offered in a particular term. Please refer to the published schedule of classes on the MyBU Student Portal for confirmation a class is actually being taught and for specific course meeting dates and times.

• CAS CS 541: Applied Machine Learning
  Undergraduate Prerequisites: CS111 (CS112 recommended); CS132 or MA242 (or EK103); CS237 or MA581 ( or EK381.) CS365 is recommended. - Covers practical skills in machine learning including techniques for clustering, classification, regression, feature selection, and model compression. Emphasizes hands-on application of methods via programming on real- world datasets.
• CAS CS 542: Principles of Machine Learning
  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.
• CAS CS 543: Algorithmic Techniques for Taming Big Data
  Undergraduate Prerequisites: exposure to basic data structures and algorithms or consent of instruc tor. - 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. Also explores 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. Includes a final project and programming assignments in which we explore the performance of our techniques when applied to publicly available data sets. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation.
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  • Creativity/Innovation
  • Quantitative Reasoning II
• CAS CS 548: Advanced Cryptography
  Undergraduate Prerequisites: (CASCS 538) or consent of instructor. - Continuation of CASCS 538. Advanced techniques to preserve confidentiality and authenticity against active attacks, zero-knowledge proofs; Fiat-Shamir signature schemes; non-malleable public-key encryption; authenticated symmetric encryption; secure multiparty protocols for tasks ranging from Byzantine agreement to mental poker to threshold cryptography.
• CAS CS 549: Spark! Machine Learning X-Lab Practicum
  Undergraduate Prerequisites: (CASCS505 OR CASCS542 OR CASCS585) or consent of instructor. Consent provided upon successful completion of pass/fail diagnostic test that will assess student readiness to tak e the course. - The Spark! Practicum offers students in computing disciplines the opportunity to apply their knowledge in algorithms, inferential analytics, and software development by working on real-world projects provided from partnering organizations within BU and from outside. The course offers a range of project options where students can improve their technical skills, while also gaining the soft skills necessary to deliver projects aligned to the partner's goals. These include teamwork and communications skills and software development processes. All students participating in the course are expected to complete a project focused on an application of inferential analytics or machine learning, including a final presentation to the partner organization. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Ethical Reasoning, Research and Information Literacy, Teamwork/Collaboration.
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  • Ethical Reasoning
  • Research and Information Literacy
  • Teamwork/Collaboration
• CAS CS 551: Streaming and Event-driven Systems
  Undergraduate Prerequisites: CAS CS 112 and CAS CS 210; CAS CS 451 and CAS CS 460 or consent of ins tructor. - Fundamentals of stream processing and event-driven systems. Topics include Pub/Sub systems; Distributed streaming systems; Dataflow programming; Fault-tolerance and processing guarantees; State management; Windowing semantics; Complex event processing; Microservice architectures; Serverless functions; Examines current and emerging architectures and use-cases.
• CAS CS 552: Introduction to Operating Systems
  Undergraduate Prerequisites: (CASCS 112 & CASCS 210) and competency with C/C++. CASCS 350 is recommended, or consent of instructor. - Examines process synchronization; I/O techniques, buffering, file systems; processor scheduling; memory management; virtual memory; job scheduling, resource allocation; system modeling; and performance measurement and evaluation.
• CAS CS 561: Data Systems Architectures
  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.
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  • Oral and/or Signed Communication
  • Research and Information Literacy
• CAS CS 565: Algorithmic Data Mining
  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.
• CAS CS 581: Computational Fabrication
  Undergraduate Prerequisites: CAS CS 112 and CAS CS 132 or CAS MA 242; CAS 480/GRS CS 680 recommende d. - Introduces 3D printing technology and computational methods for creating physical prototypes from geometric models. Student-led paper presentations cover research from prominent Computer Graphics and Human Computer Interaction conferences. Culminates in a design project involving a computational component and physical prototyping.
• CAS CS 582: Geometry Processing
  Undergraduate Prerequisites: CAS CS 112 (or equivalent), CAS CS 132 or CAS MA 242 (or equivalent), CAS MA 225 (or equivalent). - Algorithms and data structures for digital processing of triangle meshes and point clouds. Topics include: surface smoothing, parametrization, and deformation; half- edge data structures; discretized curvature measures; and spectral analysis of surfaces. Numerical methods for linear algebra and optimization also discussed.
• CAS CS 585: Image and Video Computing
  Undergraduate Prerequisites: (CASCS132 OR CASMA242) and CASCS112 or equivalent programming experience and familiarity with calculus. - Introduction to images and video as multimedia data types and algorithms for image and video understanding based on color, shading, stereo, and motion. Topics include face recognition, human-computer interfaces, animal and vehicle tracking, and medical image analysis.
• CAS CS 595: Blockchains and their Applications
  Blockchain technology amalgamates technical tools, economic mechanisms, and system design patterns. It facilitates the construction of information systems with novel combinations of robustness, decentralization, privacy, cost, and flexibility. Beyond their initial use in cryptocurrencies such as Bitcoin, blockchains have become a promising and powerful technology in business, financial services, law, and other areas. This course covers blockchain technology in a comprehensive, systematic, and interdisciplinary way. It surveys major approaches, variants, and applications of blockchains in these areas. Beyond a solid grasp of the principles, the course aims to build familiarity with practice through numerous case studies and hands-on projects. To facilitate its interdisciplinary perspective, this course will be open to two categories of students: students with Computer Science background (graduate or advanced undergraduate), and graduate students with a substantial Business or Law background and a working knowledge of computer programming. Projects will be done in heterogeneous teams combining these categories, and will center on devising and analyzing sample applications of blockchain technology, including both prototype implementations and analysis of its business/legal implications. Topics covered: disentangling 'blockchain'; cryptographic prerequisites; assets and their representations; on-chain programming; state consensus; deployments; decentralized applications (Dapps/Web3); protocol governance; protocol revenue and business models; market structure; privacy and authorization; regulation. Notes for Questrom students: While this course is explicitly designed to accommodate Questrom students, its formal listing this year is as a Computer Science. Thus, to count as an elective towards Questrom graduate degree requirements, you need to submit a Graduate Elective Request.
• CAS CS 599: Advanced Topics in Computer Science
  Various advanced topics in computer science that vary semester to semester. Please contact the CAS Computer Science Department for detailed descriptions.
• CAS EC 501: Microeconomic Theory
  Undergraduate Prerequisites: CASEC201 or equivalent, and either CASEC505 or CASMA225, or consent of instructor. - Covers the basic concepts and mathematical methods of microeconomic theory. Topics include consumer demand and its foundation on preferences and budget constraints, economics of uncertainty and imperfect information, production theory, applied competitive equilibrium analysis, elementary game theory, and imperfect competition.
• CAS EC 502: Macroeconomic Theory
  Undergraduate Prerequisites: CAS EC 202 or equivalent, or consent of instructor. - Graduate Prerequisites: EC 202 or equivalent, or consent of instructor. - Brief overview of macroeconomics, leading to mathematical models on long-run economic growth and inflation, and on short-run fluctuations with emphasis on the role of fiscal and monetary policy. Readings from research journals; introduction to analysis of macroeconomic data.
• CAS EC 505: Elementary Mathematical Economics
  Undergraduate Prerequisites: (CASMA121) or consent of instructor. - Graduate Prerequisites: (CASMA121) or consent of instructor. - Stresses the formulation of economic problems in mathematical terms. Topics covered include partial derivation, total differentials, constrained maximization, matrix algebra, dynamic analysis, and discounting. Cannot be taken for credit by concentrators in Mathematics or Economics and Mathematics.
• CAS EC 507: Statistics for Economists
  Undergraduate Prerequisites: (CASEC203 OR CASEC303) or equivalent and elementary calculus. - Graduate Prerequisites: (CASEC203 OR CASEC303) Elementary Calculus. - Covers descriptive statistics, measures of association, dispersion, frequency distribution, probability, sampling distributions, estimation, and hypothesis tests. Introduces multivariate regression analysis, with emphasis on specification, testing, and interpretation of econometric models. Requires working with data and use of statistical software.
• CAS EC 508: Econometrics
  Undergraduate Prerequisites: (CASEC507) and for undergraduate students only, (CASEC204 or CASEC304). - Graduate Prerequisites: (CASEC507) - Standard econometric methods for empirical economic research in academic or business settings. Basic concepts: quantification of uncertainty using confidence intervals, inference of causal relationships in regressions, and prediction based on regression estimates. Working with data and use of statistical software.
• CAS EC 513: Game Theory
  Undergraduate Prerequisites: (CASMA121 OR CASMA122 OR CASMA123 OR CASMA124 OR CASMA127 OR CASM A129) or instructor's permission. - Mathematical models of decision-making and strategic interactions: basic equilibrium notions in normal form games, including signaling games and repeated games. Applications include auctions, foreign policy, takeover bids, entry deterrence, cooperation and conflict, financial markets, and public goods.