UB - University at Buffalo, The State University of New York Computer Science and Engineering

CSE 694: Topics in Algorithms - Probabilistic Analysis and Randomized Algorithms

Graph and Combinatorial Algorithms

Probabilistic analysis and randomized algorithms have become an indispensible tool in virtually all areas of Computer Science, ranging from combinatorial optimization, machine learning, data streaming, approximation algorithms analysis and designs, complexity theory, coding theory, to communication networks and secured protocols. This course has two major objectives: (a) it introduces key concepts, tools and techniques from probability theory which are often employed in solving many Computer Science problems, and (b) it presents many examples from three major themes: computational learning theory, randomized/probabilistic algorithms, and combinatorial constructions and existential proofs.

In addition to the probabilistic paradigm, students are expected to gain substantial discrete mathematics problem solving skills essential for computer scientists and engineers.

This course was formerly called CSE 594.

Ph.D.:

None.

M.S.:

This course fulfills one Theory/Algorithms Core Area (Depth) requirement.

CSE 531 or equivalent, good grasp of discrete mathematic thinking. Rudimentary knowledge of discrete probability theory.

Course Instances
Semester Section Title Instructor Credit Hours Enrolled
Fall 2011 LEC Probabilistic Analysis and Randomized Algorithms Dr. Hung Ngo 3 4/30
Spring 2011 LEC Topics In Algorithms Dr. Hung Ngo 3 9/30
Fall 2009 LEC Topics In Algorithms Dr. Hung Ngo 3 0/ 0
Fall 2008 LEC Topics In Algorithms Dr. Hung Ngo 3 4/30
Spring 2008 LEC Topics In Algorithms Dr. Hung Ngo 3 9/30
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