Information and learning process

I have recently completed participation in the academic course "introduction to information and learning process" given by (the one and only) Prof. Tali Tishby.
The course introduces the subjects of: Statistical (Bayesian) decision theory, parameter estimation, PAC learning, information theory and some more. I enjoyed the course as it both provides practical intuitions and formal tools to approach problems such as estimation with hidden variables, what are the computational bounds on learning and what are the theoretical constraint on information transfer. 

For the sake of future generations, I upload my course materials to this online folder. Especially noteworthy in these are my solutions to questions on:

1. Neyman-pearson lemma, ROC curve, KL divergence and sequential probability ratio test
2. Optimal Bayes estimator and maximum likelihood estimator
3. Fisher information, Cramer-Rao bound and polynomial regression
4. Data processing inequality, entropy of stationary Markov chain, Redundancy-synergy measure and Expectation maximization