Computer
Science and Engineering SUNY at Buffalo 
CSE 694: Topics in Algorithms  Probabilistic Analysis
and Randomized Algorithms
Instructor: Hung
Q. Ngo 
Fall 2008TTh, 9:3010:50amCapen 260 (map) 
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. 
Prerequisites
CSE 531 or equivalence, good grasp of discrete mathematic thinking.. Rudimentary knowledge of discrete probability theory. 
Teaching staff and related info

Textbook Michael Mitzenmacher and Eli Upfal, Probability and Computing, Cambridge University Press, 2005. 
Reference
books: helpful, but not required.
