Graduate Computer Science

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Graduate Computer Science

2017 Fall Term


  • This course listing is informational and does not guarantee availability for registration.
  • Please click through to view the class schedule to see sections offered for your selected term.
  • Sections may be full or not open for registration. Please use WINS if you wish to register for a course.

3 Units


Computer Science 732

This course provides a broad introduction to machine learning and pattern recognition. Topics include but are not limited to Bayesian Inference, SVMs, Clustering and Classification, Decision Trees and Ensemble Methods. Particular focus will be placed on the theoretical understanding of these methods, as well as their practical applications.

3 Units


Computer Science 733

This course introduces students to advanced techniques for the design and analysis of algorithms, and explores a variety of applications. Techniques to be covered incude graph representation & graph traversal, shortest path, minimum spanning tree, linear programming, network flow, randomization, and approximation algorithms. NP-complete problems and reductions will also be studied.

3 Units


Computer Science 764

The purpose of this course is to understand the core technical ideas and concepts in designing and using cloud computing systems, covering a broad range of topics that include cloud system architectures, cloud storage and management, cloud programming frameworks, virtualization and resource management, and datacenter networks. It is a blend of lecture, paper readings/presentations, and programming practice using a cloud.

3 Units


Computer Science 766

This course covers advanced database management system design principles and techniques. Course material includes both fundamental principles and current research. Possible topics include query processing and optimization, transaction processing, distributed databases, object-oriented databases, data warehousing, and data mining.

1-3 Units


Computer Science 798

Study of a selected topic or topics under the direction of a faculty member.

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