Introduction
Information theory is the mathematical study of the quantification, storage, and communication of information. The field was originally established by the works of Harry Nyquist and Ralph Hartley, in the 1920s, and Claude Shannon in the 1940s. The field, in applied mathematics, is at the intersection of probability theory, statistics, computer science, statistical mechanics, information engineering, and electrical engineering. A key measure in information theory is entropy. Entropy quantifies the amount of uncertainty involved in the value of a random variable or the outcome of a random process.
Contents
Basics of Information
Information Content and Entropy
Coding and Compression
Shannon Coding
Huffman Coding
Shannon-Fano-Elias and Interval Coding
Free Energy Principle
More …
Information Geometry
Machine Learning
Acknowledgement
This part is mainly based on ANU course COMP2610, and textbook MacKay, Information Theory, Inference, and Learning Algorithms, convened by Prof. Thushara Abhayapala.