Lectures related to Entropy and Data Compression: Huffman Coding Techniques

Explores convergence in law for random variables, including Kolmogorov's theorem and proofs based on probability lemmas.

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Covers probability, statistics, independence, covariance, correlation, and random variables.

Explores entropy bounds, conditional entropy theorems, and the chain rule for entropies, illustrating their application through examples.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Explores the Law of Total Probability and its applications in real-world scenarios, introducing key concepts in probability theory.