Science and Engineering
SUNY at Buffalo
CSE 694: Topics in Algorithms - Probabilistic Analysis and Randomized AlgorithmsInstructor: Hung Q. Ngo
Fall 2008T-Th, 9:30-10:50am
Capen 260 (map)
|Teaching Staff||Contact and Administrative Information|
|Hung Q. Ngo (instructor)||
Office: 238 Bell Hall
Probabilistic analysis and randomized algorithms have become an indispensible tool in virtually all areas of Computer Science, ranging from combinatorial optimization, machine learning, approximation algorithms analysis and designs, data streaming, 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 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.
Basic Algorithm course (CSE 531), a good sense of discrete mathematics thinking, and rudimentary knowledge of probability theory.
The desire and ability to learn new ideas quickly.
At the end of this course, each student should be able to: