Lectures in course COM-404: Information theory and coding

Covers the fundamentals of information theory, focusing on source coding and channel coding.

Covers data compression techniques, including source coding and unique decodability concepts.

Covers source coding, injective codes, prefix-free codes, and Kraft's inequality.

Covers source coding, Kraft's inequality, mutual information, Huffman procedure, and properties of tropical sequences.

Covers source coding, typical sequences, stationarity, and efficient encoding in information theory.

Covers expected code word length, Huffman procedure, and entropy in coding theory.

Covers the Lempel-Ziv universal coding algorithm and invertible finite state machines in information theory.

Covers the Universal Compression using the Lempel-Ziv method and demonstrates its superiority over other methods.

Explores entropy in information theory and its role in data processing and probability distributions.

Covers prefix-free codes, Kraft inequality, Huffman coding, and entropy.

Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.

Covers the formation theory and coding, focusing on channel capacity and concave functions.

Covers the proof and properties of the coding theorem, focusing on maximizing the properties of lx and the achievable rate.

Explores random coding achievability and proof variants in information theory, emphasizing achievable rates and architectural principles.

Explores mutual information and entropy calculation between random variables.

Covers concepts of entropy, Gaussian distributions, and channel capacity with constraints.

Explores the capacity of communication channels with Gaussian noise and noise impact.

Covers error correction codes theory and applications, emphasizing the importance of minimizing distance for reliable communication.

Explores binary channel decoding and vector spaces in coding theory.

Explores the Hamming Code for error correction, emphasizing its ability to correct single-bit errors.