Lectures related to Information Theory: Source Coding & Channel Coding

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.

Conditional Entropy and Data Compression Techniques

Discusses conditional entropy and its role in data compression techniques.

Source Coding Theorems: Entropy and Source Models

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

Entropy and Algorithms: Applications in Sorting and Weighing

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

Compression: Prefix-Free Codes

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

Conditional Entropy and Information Theory Concepts

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

Compression: Kraft Inequality

Explains compression and Kraft inequality in codes and sequences.

Information Theory and Coding

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

Stochastic Processes: Sequences and Compression

Explores compression in stochastic processes through injective codes and prefix-free codes.

Source Coding and Prefix-Free Codes

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

Lecture: Shannon

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

Information Theory Basics

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

Information Theory: Review and Mutual Information

Reviews information measures like entropy and introduces mutual information as a measure of information between random variables.

Random Variables and Information Theory Concepts

Introduces random variables and their significance in information theory, covering concepts like expected value and Shannon's entropy.

Information Theory: Channel Capacity and Convex Functions

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

Compression: Prefix-free Codes

Explains how to design efficient prefix-free codes for compression.

Source Coding Theorem

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

Probability and Statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Entropy and Information Theory

Explores entropy, uncertainty, coding theory, and data compression applications.

Probability Distributions in Environmental Studies

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.