Graduate Computer Science
2017 Fall Term
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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.
ADVANCED ALGORITHM DESIGN AND ANALYSIS
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.
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.
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.
Computer Science 798
Study of a selected topic or topics under the direction of a faculty member.