Master tools from probability and random processes for solving engineering problems in communications and machine learning applications.

Description

Random Processes with Applications to Communications and Machine Learning: probability theory, random variables, random processes, estimation, convergence, concentration bounds, Markov chains, Martingales.

Prerequisites

Basic understanding of probability theory and combinatorics from undergraduate courses.

Recommended literature

Lecture notes will be provided. The lecture notes are based on the following textbook, which will also be used for the homework: - H. Stark and J. W. Woods, Probability, Statistics, and Random Processes for Engineers, Prentice Hall; 4th edition (August 20, 2011). The following additional literature is recommended: - B. Hajek, An Exploration of Random Processes for Engineers. - G. Grimmett and D. R. Stirzaker, Probability and Random Processes, Oxford Uni. Press. - G. Grimmett and D. R. Stirzaker, One Thousand Exercises in Probability, Oxford Uni. Press. - A. Papoulis and S. U. Pillai, Probability, Random Variables, and Stochastic Processes, McGraw-Hill, Fourth or latest Edition. - D. A. Levin and Yuval Peres, Markov Chains and Mixing Times. Vol. 107. American Mathematical Society. Additional online references: - R. G. Gallager, Stochastic Processes: Theory for Applications. (Video lectures available on Youtube). - B. Hajek, An Exploration of Random Processes for Engineers - R.M. Gray and L.D. Davisson, Introduction to Statistical Signal Processing