UB - University at Buffalo, The State University of New York

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.

http://www.cse.buffalo.edu/~hungngo/classes/2011/Fall-694

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