Probability & Computing: Randomized Algs and Markov Chains Course ID 15659 Description Probability theory has become indispensable in computer science. In areas such as artificial intelligence and computer science theory, probabilistic methods and ideas based on randomization are central. In other areas such as networks and systems, probability is becoming an increasingly useful framework for handling uncertainty and modeling the patterns of data that occur in complex systems. This course is a follow-up course to 15-259, Probability and Computing. It will cover Chapters 18-27 of the same textbook, "Introduction to Probability for Computing", by Prof. Harchol-Balter. Topics include concentration inequalities, various randomized algorithms including number theoretic routines, Markov chains and their many applications, and queueing theory. The course will assume familiarity with multivariate calculus and linear algebra. Course Relevance 15-659 is for graduate students. Undergraduates should enroll in 15-359. Pre-required Knowledge B or better in 15-559; familiarity with 3-D calculus and linear algebra
