Explores probability convergence, discussing conditions for random variable sequences to converge and the uniqueness of convergence.
Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.
Covers entropy, source coding, encoding maps, decodability, prefix-free codes, and Kraft-McMillan's inequality.
Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.
Explores channel coding, BICM, and LLRs in wireless communication systems, emphasizing the importance of error detection and correction.
