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In this course, students learn and gain practical experience with software engineering principles and techniques. The practical experience centers on a semester-long team project in which a software development project is carried through all the stages of the software life cycle. Topics in this course include requirements analysis, specification, design, abstraction, programming style, testing, maintenance, communication, teamwork, and software project management. Particular emphasis is placed on communication and negotiation skills and on designing and developing maintainable software.

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Machine learning is the computational study of artificial systems that can adapt to novel situations, discover patterns from data, and improve performance with practice. This course will cover the mathematical foundations of supervised and unsupervised learning. The course will provide a state-of-the-art overview of the field, with an emphasis on implementing and deriving learning algorithms for a variety of models from first principles.

The theory seminar is a weekly meeting in which topics of interest in the theory of computation - broadly construed - are presented. This is sometimes new research by visitors or local people. It is sometimes work in progress, and it is sometimes recent material of others that some of us present in order to learn and share. This is a one-credit seminar which may be taken repeatedly for credit. May be repeated for credit up to six times.

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This seminar examines recent work in explanatory and tutoring systems. Participants study artificial intelligence in education, a young field that explores theories about learning, and explores how to build software that delivers differential teaching as it adapt its response to student needs and domain knowledge. Such software supports people who work alone or in collaborative inquiry, students who question their own knowledge, and students who rapidly access and integrate global information.

The design and analysis of efficient algorithms for important computational problems. Paradigms for algorithm design including Divide and Conquer, Greedy Algorithms, Dynamic Programming; and, the use of Randomness and Parallelism in algorithms. Algorithms for Sorting and Searching, Graph Algorithms, Approximation Algorithms for NP Complete Problems, and others. Prerequisites: The mathematical maturity expected of incoming Computer Science graduate students, knowledge of algorithms at the level of COMPSCI 311.

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