Lectures related to Information Theory: Source Coding, Cryptography, Channel Coding

Covers the basics of information theory, focusing on Shannon's setting and channel transmission.

Explores channel coding with a focus on convolutional codes, emphasizing error detection, correction, and decoding processes.

Compression: Prefix-Free Codes

Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.

Conditional Entropy and Data Compression Techniques

Discusses conditional entropy and its role in data compression techniques.

Information Theory Basics

Introduces information theory basics, including entropy, independence, and binary entropy function.

Conditional Entropy and Information Theory Concepts

Discusses conditional entropy and its role in information theory and data compression.

Entropy and Data Compression: Huffman Coding Techniques

Discusses entropy, data compression, and Huffman coding techniques, emphasizing their applications in optimizing codeword lengths and understanding conditional entropy.

Source Coding Theorem

Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.

Channel Coding and BICM (LLRs)

Explores channel coding, BICM, and LLRs in wireless communication systems, emphasizing the importance of error detection and correction.

Data Compression and Shannon-Fano Algorithm

Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.

Entropy and Algorithms: Applications in Sorting and Weighing

Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.

Error Correction Codes: Basics

Introduces erasure and error channels, Hamming distance, and error correction codes.

Data Compression and Shannon's Theorem Summary

Summarizes Shannon's theorem, emphasizing the importance of entropy in data compression.

Source Coding Theorems: Entropy and Source Models

Covers source coding theorems, entropy, and various source models in information theory.

Information Theory: Basics and Applications

Covers the basics of information theory and its applications in various fields.

Information Coding: Source, Cryptography, Channel

Covers source coding, cryptography, and channel coding for communication systems.

Public-Key Cryptography: Standards and Applications

Discusses public-key cryptography, focusing on standards like RSA, DSA, and AES, and their applications in secure communications.

Source Coding: Compression

Covers entropy, source coding, encoding maps, decodability, prefix-free codes, and Kraft-McMillan's inequality.

Information Theory: Source Coding & Channel Coding

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

Information Measures: Entropy and Information Theory

Explains how entropy measures uncertainty in a system based on possible outcomes.