Prerequisites: two semesters of Calculus, one semester of Probability and Statistics. Mathematical topics necessary for graduate study in computer science: review of sets, relations, functions; elementary combinatorics; summation calculus, recurrences, generating functions; logic; graphs and trees. This is a remedial course which does not carry credit towards a Computer Science degree.
An advanced introduction to theoretical computer science. This course will cover the fundamentals of automata, formal languages, and computability theory.
Pre-requisite: 91.404 Analysis of Algorithms
Advanced algorithms and complexity analysis. Dynamic programming; greedy algorithms; amortized analysis; shortest path and network flow graph algorithms; NP-completeness; approximation algorithms; number-theoretic algorithms; string matching; computational geometry. Additional topics may include linear programming, parallel algorithms, fast Fourier transforms, polynomial, integer, and matrix algorithms. Readings may include conference and journal papers from the algorithms literature.Abstract types, lists, trees, graphs, sets; relevant algorithms and their worst and average case analyses; fast transforms; polynomial, integer, and matrix algorithms; NP-completeness.
Pre-requisite: 91.404 Analysis of Algorithms
Advanced algorithms topics, such as design and analysis of geometric and combinatorial algorithms, computability andcomplexity.
Pre-Requisite: 91.503 Algorithms, or permission of instructor
Topics of mutual interest to the instructor and students(s) Graph and Network Algorithms. A review of basics followed by a selection of more advanced topics: the graph isomorphism problem and structural complexity; probabilistic graphs and algorithms; parallel graph algorithms. Emphasis will be placed on investigation and experimentation with implementation of algorithms based on material from Knuth and Mathematica, and possibly additional material from other sources (e.g. DIMACS, etc.)
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This course is a survey of Web programming technologies. It begins with a discussion of what Web servers and clients are, how they interact, and how one sets them up. We then explore a wide variety of Web technologies including HTML, JavaScript, JavaServer Pages, Java Servlets, and XML and its many related technologies. Our goal in this course is to provide the basic understanding and knowledge of how the Internet and World Wide Web operate and the technical knowledge required to establish and maintain an Internet/Web site and to develop and introduce new capabilities and features on such sites.
A continuation of 91.513 with a focus on current topics and topics of special interest. Examples of recent topics include: The semantic Web and ontologies, Web services, Peer-to-peer networks, Information Search and Retrieval, Autonomous intelligent agents and Multi-modal presentations.
Pre-Requisite: 91.513 Internet & Web Systems I
This course provides insight into multiprocessing operating systems including processor memory, peripheral, and file systems management in batch, timesharing, real time, and distributed systems targeted for various hardware. Particular emphasis will be placed on techniques of virtual memory as well as the problems of concurrency in both centralized and distributed systems. An OS simulation is a required programming project. Some topics to be covered are process synchronization; high-Level mechanisms for concurrency; processor scheduling and system analysis; deadlock; virtual memory; distributed systems; computer security.
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The design and implementation of an interactive multiprocessing operating system to run on a bare hardware system. Separate teams manage the major subsystems with in-class design reviews to coordinate system integration. A functioning system is a class requirement.
Pre-Requisite: 91.515 Operating Systems I
This course will focus on existing and proposed technologies for storing digital information. Both hardwre and software issues will be examined, beginning with device and controller organization and proceeding through aggregation techniques, interconnect architectures and host consideration. At each level, specific components will be evaluated with respect to critical storage criteria, such as bandwith and latency, fault tolerance, infrastructrure requirements and cost.
Object-oriented techniques for analysis, specification, and design. Static information models and state-based dynamic behavior models applied to rapid prototyping projects that both use and implement object-oriented CASE tools.
Continuation of 91.522; a team-based project course that applies object-oriented methods to designing, implementing, and maintaining interactive and distributed software systems with emphasis on quality and reusability. (Undergraduates may substitute this course for 91.412.)
An examination of the factors that contribute to well-engineered user interfaces for a wide variety of programs. Consideration of screen design, programming technique, and input devices. Review of human factors literature and development of skills for designing and evaluating user interfaces.
Topics of mutual interest to the instructor and student(s).
A one-semester course designed to provide students with hands-on understanding of the underlying concepts of programming languages, the principles of their design, and the fundamental methods for their implementation. An executable metalanguage such as Scheme or SML is used throughout the course, facilitating the design of high-level, concise interpreters that are easy to comprehend. The approach is analytical because the salient features of the imperative, functional, object-oriented, and logic programming paradigms are described in the executable meta-language.
Pre-Requisites: 91.301 Org Programming Languages or 91.406 Compiler Construction I
This course implements a compiler for a complete language. Topics include grammars, syntax, elements of parsing and recursive descent, semantics, basic code generation, fast compilation runtime support. Programming project required.
Consistent and complementary definitions of programming languages. Axiomatic, operational, denotational, translational, and other semantic approaches. Formal program specification. Verification using the techniques of Floyd and Hoare.
Topics of mutual interest to students and instructor.
This course looks at classical and novel methodologies for the visualization of large and complex data sets. The course covers both scientific and information visualization starting with data modeling, human perception and cognition, basic and advanced techniques, interaction, formal models, real time systems, and frameworks for integrated analysis and visualization. Examples used come from numerous areas including the biomedical literature and security.
Search and games, knowledge representation paradigms, natural language understanding, planning, perception. Use of the LISP language for one or more programming projects.
This introductory machine learning course will give an overview of many models and algorithms used in modern machine learning, including decision-tree and rule-based learning, statistical learning, neural networks, hypothesis evaluation, support vector machines, Bayesian belief networks, genetic algorithms, clustering, ensemble methods, explanation-based learning and reinforcement learning. The course will give the student the basic ideas and intuition behind these methods, as well as a more formal understanding of how and why they work. We will also read papers on current machine learning research and papers on how discoveries are made by human scientists. Students will have an opportunity to experiment with machine learning techniques and apply them a selected problem in the context of a term project.
Introduction to the hardware, software and mathematics of 2- and 3-dimensional interactive computer graphics systems, including standards, modeling, transformations, hidden-surface removal, shading, and realism.
Lighting models, photo-realism, animation, constructive solid geometry, and distributed graphics.
A broad interpretation of robotics to mean systems that interact with people, each other, and the world around them, using sensors, actuators, communications, and a control program. Project- and lab-based course that involves electronics, embedded coding, mechanical design, and research.
This course will focus on the artificial intelligence side of robotics in a project- and lab-based course. Topics to be covered include robot architectures, mapping and localization, learning, vision, multi-agent systems and current research areas.
Topics of mutual interest to the instructor and student(s).
An advanced study of computer system organization. Topics include data-path design, control, ALU's, memory organization, distributed processing, theories of parallel computing, advanced architectures, computer communication.
A survey of parallel computer architectures, parallel programming languages, and parallel algorithms, with emphasis on solving practical problems with parallel computers. A final project, typically a substantial parallel program, is required. Usually offered during the Spring semester.
The two main topics are routing and transport functions, and ATM networks. Routing and transport layer functions in conventional data networks will be examined, with a heavy emphasis on the TCP/IP protocol suite. The ATM is seen as a promising technology for integrated voice/data/video services. The concept of the ATM network will be reviewed. Traffic management functions in ATM networks will explored in detail, including analytic representation of traffic bandwidth and congestion control techniques.
Pre-Requisite: 91.563 Data Communications I
Topics of mutual interest to the instructor and student(s)
Resource sharing; computer traffic characterizations; multiplexing; network structure; packet switching and other switching techniques; design and optimization; protocols; routing and flow control; simulation and measurement; communications processors.
Continuation of 91.563
Pre-Requisite: 91.563 Data Communications I
Topics of mutual interest to the instructor and student(s).
Pre-Req: 91.555 Computer Networks or 91.563 Data Communication I
Study of various database models including hierarchical, network, relational, entity-relationship, and object-oriented models. This course also covers data design, integrity, security, concurrency, recovery, query processing, and distribution.
Continuation of Data Base I. Various issues in the implementation of database systems will be covered.
Pre-requisite: 91.573 Data Base I
Topics of mutual interest to the instructor and student(s).
Topics of mutual interest to the instructor and student(s).
This course covers advanced topics in approximation algorithms for NP-hard problems, including combinatorial algorithms and LP-based algorithms for set cover, k-cut, k-center, feedback vertex set, shortest superstring, knapsack, bin packing, maximum satisfiability, scheduling, Steiner tree, Steiner Forest, Steiner network, facility location, k-median, semidefinite programming. It also covers counting problems, shortest vector, hardness of approximation, and open problems for research.
Pre-Requisite: 91.503 Algorithms
