Computer
Science and Engineering SUNY at Buffalo 
CSE 694: Topics in Algorithms  Probabilistic Analysis
and Randomized Algorithms
Instructor: Hung
Q. Ngo 
Spring 2008MWF, 22:50pmTalbert 106 (map) 
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, complexity theory, coding theory, to communication networks and secure protocols. This course has two major objectives: (a) it introduces key concepts, tools and techniques from probability theory which are often employed in the development of probabilistic algorithms and analyses and in the constructions of combinatorial objects, and (b) it gives a glance of how these concepts and techniques are used in algorithm analysis and design and combinatorial constructions, among other usages. In addition to the probabilistic paradigm, students are expected to gain substantial discrete mathematics problem solving skills essential for computer engineers. 
Prerequisites
CSE 531 or equivalence, good grasp of discrete mathematic thinking.. Rudimentary knowledge of discrete probability theory. 
Teaching staff and related info

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