- Good understanding of basic probability (e.g. ECE302)
- Some familiarity with Markov chains (e.g. EECS454/EECS422/EECS495 Randomized Algorithms/IEMS460)
- S. P. Meyn and R. L. Tweedie, Markov chains and stochastic stability, Second edition, Cambridge University Press, Cambridge, 2009.
- J. R. Norris, Markov chains, Cambridge Series in Statistical and Probabilistic Mathematics, 2, Cambridge University Press, Cambridge, 1998.
- N. Cesa-Bianchi and G. Lugosi, Prediction, learning, and games, Cambridge University Press, Cambridge, 2006.
- P. R. Kumar and P. Varaiya, Stochastic systems: Estimation, identification and adaptive control, Prentice Hall, Englewood Cliffs, N. J., 1986.
- M. Mitzenmacher and E. Upfal, Probability and computing: Randomized algorithms and probabilistic analysis, Cambridge University Press, Cambridge, 2005.
- Relevant papers.
EXAMS: There will be no exam for this course. However, there will be a final project.
COURSE GRADE: Your final grade will be determined by a mixture of class participation, problems sets and final project. The weightings used will be the following.
- Problem sets 20%
- Class participation 20%
- Final project 60%