Graduate classes, Fall 2017, Computer Science

CS 526: AlgorithmsCredits: 3− Description− Sections
Content: This course is a graduate level introduction to the design and analysis of algorithms. Although we will review some undergraduate level material, we will instead emphasize reading and experimentation at a level appropriate for the initiation of research. This course will have both theoretical and practical content. As course highlights, students will be expected to implement and analyze the performance of a fundamental data structure, starting with a close reading of the original research paper.
Texts: TBA
Assessments: There will be about six regular homework assignments, including both written and programming components, counting for at least half of the grade. There will be a take-home mid-term exam and a take-home final exam. The examinations will emphasize both analysis and reading.
Prerequisites: CS 224 and CS 253.
1MSC: W303MW 1:00pm - 2:15pmMichelangelo Grigni
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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Assessments: TBA
Prerequisites: Cross-listed with BIOS 534. 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.
1MSC: W301TuTh 1:00pm - 2:15pmJoyce Ho
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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1MSC: W301TuTh 2:30pm - 3:45pmKen Mandelberg
CS 556: Compiler ConstuctionCredits: 3− Description− Sections
Content: An introduction to the algorithms and data structures used to construct a high level language compiler. Topics include: formal language specification, lexical analysis, parsing, and code generation.
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Assessments: A substantial portion of this course will involve a student project to construct a compiler for a simplified programming language. Working knowledge of C or C++ is highly recommended.
Prerequisites: CS 323, CS 255, and CS 424, or their equivalents.
1MSC: W303MW 11:30am - 12:45pmJames Lu
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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Assessments: TBA
Prerequisites: Undergraduate level of Artificial Intelligence or Machine Learning.
1MSC: W303MW 4:00pm - 5:15pmEugene Agichtein
CS 563: Digital Image ProcessingCredits: 3− Description− Sections
Content: TBA
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1MSC: W303TuTh 11:30am - 12:45pmJun Kong
CS 570: Data MiningCredits: 3− Description− Sections
Content: This course offers an introduction to data mining concepts and techniques. The goal is for the students to have a solid foundation in data mining that allows them to apply data mining techniques to real-world problems and to conduct research and development in new data mining methods. Topics include data processing, design and implementation of data warehouse and OLAP systems, data mining algorithms and methods including association analysis, classification, cluster analysis, as well as emerging applications and trends in data mining.
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Assessments: Some familiarity with a programming language, such as Java or C++, is required for programming assignments and/or final project. Some knowledge about database systems and statistics will be helpful.
Prerequisites: TBA
1MSC: W301MW 10:00am - 11:15amLi Xiong
CS 584: Topics in Computer Science: Computer SecurityCredits: 3− Description− Sections
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1MSC: W301MW 2:30pm - 3:45pmYmir Vigfusson
CS 584: Topics in Computer Science: Advanced SystemsCredits: 3− Description− Sections
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2MSC: W304TuTh 10:00am - 11:15amAvani Wildani
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 different audiences.
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1MSC: W303F 2:00pm - 2:50pmJames Lu
CS 597R: Directed Study: Professional DevelopmentCredits: 1− Description− Sections
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4MSC: W306F 2:00pm - 2:50pmShun Yan Cheung
CS 700R: Graduate SeminarCredits: 1− Description− Sections
Content: This is a required course for all students in the PhD program. It comprises seminars given by faculty, invited guests, and students.
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Assessments: This course is a one-credit course; the grading basis is S/U only.
Prerequisites: TBA
1MSC: W201F 3:00pm - 3:50pmEugene Agichtein