CSE 694: Probabilistic Analysis and Randomized Algorithms
This page refers to the Fall 2011 offering of CSE 694 only. The information on this page does not necessarily apply to every offering of CSE 694.
Fall 2011
37276
Probabilistic Analysis, Statistical Learning Theory, and Randomized 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 these major themes: combinatorial constructions and existential proofs, randomized algorithms, sampling, and statistical learning theory. In addition to the probabilistic paradigm, students are expected to gain substantial discrete mathematics problem solving skills essential for computer scientists and engineers.
CSE 531 or equivalence, good grasp of discrete mathematic thinking. Rudimentary knowledge of discrete probability theory.
Ph.D.: This course does not fulfill core area or core course requirements.
M.S.: This course fulfills one Theory/Algorithms Core Area requirement.
