Computer Science and Engineering
SUNY at Buffalo

CSE 694: Topics in Algorithms - Probabilistic Analysis and Randomized Algorithms

Instructor: Hung Q. Ngo

Fall 2008

T-Th, 9:30-10:50am
Capen 260 (map)

General Information | Schedule and Notes | Assignments | Syllabus | Helpful Links
Brief Course Description

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
  • Instructor
    • Hung Q. Ngo ( hungngo [at] cse [dot] buffalo [dot] edu )
    • Office Hours: T/Thu 11am--noon

Textbook

Michael Mitzenmacher and Eli Upfal, Probability and Computing, Cambridge University Press, 2005.

CLRS BOOK
Reference books: helpful, but not required.
  • Rajeev Motwani and Prabhakar Raghavan, Randomized Algorithms, 492 pages, Cambridge University Press (August 25, 1995), ISBN: 0521474655
  • Alon, Noga; Spencer, Joel H. The probabilistic method. Second edition. Wiley-Interscience Series in Discrete Mathematics and Optimization. John Wiley & Sons, New York, 2000. xviii+301 pp. ISBN: 0-471-37046-0
  • Bella Bolobas, Random Graphs (2nd ed.), Cambridge University Press, Studies in Advanced Mathematics, #73, September 2001.
  • Molloy, Michael; Reed, Bruce Graph colouring and the probabilistic method. Algorithms and Combinatorics, 23. Springer-Verlag, Berlin, 2002. xiv+326 pp. ISBN: 3-540-42139-4
  • Gamerman, D. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. Boca Raton, FL: CRC Press, 1997.
  • W. R. Gilks, D. J. Spiegelhalter, Sylvia Richardson, Markov Chain Monte Carlo in Practice, CRC Press, Feb 1996.