Computer Science

Course introduces the mathematical foundations of machine learning, including theoretical models of machine learning, and the design and analysis of learning algorithms. Topics include: bounds on the number of random examples needed to learn; learning from non-random examples in the on-line learning model (i.e., for investment portfolio selection); how to boost the accuracy of a weak learning algorithm; learning with queries; Fourier-based algorithms and support-vector machines.

Advanced studies in selected areas in signal processing, communication and information theory, decision and control, and system theory. Emphasis on recent developments and current literature. Content varies from year to year according to the instructor's and students' interests.

A study of the essential tools for analyzing the vast amount of data that have become available in modern scientific research. Mathematical foundations of the field will be studied, along with the methods underlying the current state of the art. Probabilisitc graphical models and a unifying formalism for descrtibing and extending previous methods from statistics and engineering will be considered. Prerequisites COS402 or COS424. Undergraduates by permission only.

An introduction to algorithmic techniques for reasoning about software. Basic concepts in logic-based techniques including model checking, invariant generation, symbolic execution, and syntax-guided synthesis; automatic decision procedures in modern solvers for Boolean Satisfiability (SAT) and Satisfiability Modulo Theory (SMT); and their applications in automated verification, analysis, and synthesis of software. Emphasis on algorithms and automatic tools.

Survey of operating systems covering: early systems, virtual memory, protection, synchronization, process management, scheduling, input/output, file systems, virtual machines, performance analysis, software engineering, user interfaces, distributed systems, networks, current operating systems, case studies. Survey of research papers from classic literature through contemporary research. Prerequisite: COS 318 or equivalent.

Introduction to research in computational complexity theory. Computational models: nondeterministic, alternating, and probabilistic machines. Boolean circuits. Complexity classes associated with these models: NP, Polynomial hierarchy, BPP, P/poly, etc. Complete problems. Interactive proof systems and probabilistically checkable proofs: IP=PSPACE and NP=PCP (log n, 1). Definitions of randomness. Pseudorandomness and derandomizations. Lower bounds for concrete models such as algebraic decision trees, bounded-depth circuits, and monotone circuits.

Advanced topics in computer graphics, with focus on learning recent methods in rendering, modeling, and animation. Appropriate for students who have taken COS426 (or equivalent) and who would like further exposure to computer graphics.

Data structures and algorithms for graph and network problems, including disjoint set union, heaps, search trees, search on graphs, minimum spanning trees, shortest paths, network flows, and matchings. The intent of the course is to examine the most efficient algorithms known for a variety of combinatorial problems and to discover the principles underlying the design and analysis of these algorithms. The emphasis is on asymptotic worst-case and amortized analysis. Prerequisite: 423 or the equivalent.

Introduces students to computational issues involved in analysis and display of large-scale biological data sets. Algorithms covered will include clustering and machine learning techniques for gene expression and proteomics data analysis, biological networks, joint learning from multiple data sources, and visualization issues for large-scale biological data sets. No prior knowledge of biology or bioinformatics is required; an introduction to bioinformatics and the nature of biological data will be provided. In depth knowledge of computer science is not required, but students should have some understanding of programming and computation.

Survey of computer networks covering end-to-end principle, multiplexing, virtualization, packet switching vs. circuit switching, router design, network protocols, congestion control, internet routing architecture, network measurement, network management, and overlay networks. Survey of research papers from classic literature through contemporary research.

Course covers pivotal developments in computing, including hardware, software, and theory. Material will be covered by reading seminal papers, patents, and descriptions of highly-influential architectures. Course emphasizes a deep understanding of the discoveries and inventions that brought computer systems to where they are today, and class is discussion-oriented. Final project or paper required. Graduate students and advanced undergraduates from ELE, COS, and related fields welcome.

Natural Language Processing (NLP) is witnessing exciting developments in core technologies and applications to a wide variety of domains. This graduate-level course focuses on an advanced study of frameworks, algorithms and methods in NLP -- including state-of-the-art techniques for problems such as language modeling, text classification, machine translation, and question answering. The course contains multiple programming assignments, paper readings, a mid-term and a final project. Students are expected to have taken at least one introductory course in machine learning prior to this class, and be comfortable with programming in Python.

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One-term full time research internship at a host institution to perform scholarly research directly relevant to a student's dissertation work. Research objectives will be determined by the student's advisor in consultation with the outside host. Monthly progress reports and a final paper are required. Enrollment is limited to post-generals students. Students will be permitted to enroll in this one-semester course at most twice. Participation will be considered exceptional.

Advanced studies in selected areas in signal processing, communication and information theory, decision and control, and system theory. Emphasis on recent developments and current literature. Content varies from year to year according to the instructor's and students' interests.

Topics involving current research in computer science and applications in other fields.

Topics involving current research in computer science and applications in other fields.

Topics involving current research in computer science and applications in other fields.

Topics involving current research in computer science and applications in other fields.

Topics involving current research in computer science and applications in other fields.

Topics involving current research in computer science and applications in other fields.

Topics involving current research in computer science and applications in other fields.

An in-depth study of compiler backend techniques for modern computer architectures. The course includes coverage of the fundamentals and a survey of current research. Students work together on a research project. Familiarity with both computer architecture and compilers is recommended.

This seminar prepares computer science graduate students and advanced undergraduate students to effectively engage on matters of public policy and law. The core of the course is a survey of computer science research that has successfully influenced government decision making or commercial practices. Topics include consumer privacy, data security, net neutrality, government surveillance, artificial intelligence fairness, election integrity, and cryptocurrencies. Assignments challenge participants to develop their own research interests into deliverables for public policy and law audiences.

Topics involving current research in computer science and applications in other fields.