Graduate classes, Fall 2016, Computer Science

CS 534: Machine LearningCredits: 3− Description− Sections
Content: This course covers fundamental machine learning theory and techniques. The topics include basic theory, classification methods, model generalization, clustering, and dimension reduction. The material will be conveyed by a series of lectures, homeworks, and projects.
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Prerequisites: Knowledge of linear algebra, multivariate calculus, basic statistics and probability theory. Homework and project will require programming in Python, Matlab, C/C++ or R. Or permission by the instructor.
000MSC: W201MW 1:00pm - 2:15pmLee Coopermax 30
CS 551: Systems ProgrammingCredits: 3− Description− Sections
Content: Systems programming topics will be illustrated by use of the Unix operating system. Topics include: file i/o, the tty driver, window systems, processes, shared memory, message passing, semaphores, signals, interrupt handlers, network programming and remote procedure calls. Programming examples and assignments will illustrate the system interface on actual computer hardware. All assignments will be in written in C. The department's computing lab will be used in the course to allow students to get hands-on experience with operating system and hardware topics that cannot effectively be pursued on a central timesharing computer.
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000MSC: W301TuTh 2:30pm - 3:45pmKen Mandelbergmax 25
CS 557: Artificial IntelligenceCredits: 3− Description− Sections
Content: This course covers core areas of Artificial Intelligence including perception, optimization, reasoning, learning, planning, decision--making, knowledge representation, vision and robotics.
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Prerequisites: Undergraduate level of Artificial Intelligence or Machine Learning.
000MSC: W306TuTh 10:00am - 11:15amEugene Agichteinmax 25
CS 573: Data Privacy and SecurityCredits: 3− Description− Sections
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000MSC: W301MW 10:00am - 11:15amLi Xiongmax 30
CS 584: Topics in Computer Science: Neural ComputationCredits: 3− Description− Sections
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000MSC: E408MW 2:30pm - 3:45pmAvani Wildani
CS 584: Topics In Computer Science: Computer SecurityCredits: 3− Description− Sections
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001MSC: W303MW 11:30am - 12:45pmYmir Vigfusson
CS 590: Teaching SeminarCredits: 1− Description− Sections
Content: This course explores theoretical and practical approaches for effective teaching, with particular emphasis on the discipline of Computer Science. After this course, students will be able to demonstrate knowledge of multiple pedagogical strategies, write a syllabus, develop assessment items, and design and deliver lectures and presentations for a variety of audiences.
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000MSC: W302F 2:00pm - 2:50pmDavide Fossatimax 25
CS 597R: Directed StudyCredits: 1 - 9− Description− Sections
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00PMSCFaculty (TBA)max 999
CS 598R: Rotation ProjectCredits: 3− Description− Sections
Content: Computer Science and Informatics PhD students are required to complete two rotation projects prior to their qualifying exams and dissertation research. Projects often involve interdisciplinary work, and can be co-supervised by a Math/CS faculty and an external faculty member or researcher (e.g., Schools of Medicine and Public Health, the CDC). Students are required to submit a project proposal and a final report.
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000MSCFaculty (TBA)max 999
CS 700R: Graduate SeminarCredits: 1− Description− Sections
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000MSC: W201F 3:00pm - 3:50pmEugene Agichteinmax 40
CS 797R: Directed StudyCredits: 1 - 9− Description− Sections
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00PMSC: -----Faculty (TBA)max 99
CS 799R: Dissertation ResearchCredits: 1 - 9− Description− Sections
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00PMSC: -----Faculty (TBA)max 999