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An introduction to information Theory and Entropy
This note covers Measuring complexity, Some probability ideas, Basics of information theory, Some entropy theory, The Gibbs inequality, A simple physical example Shannon’s communication theory, Application to Biology, Examples using Bayes Theorem, Analog channels, A Maximum Entropy Principle, Application to Physics(lasers), Kullback-Leibler information measure.
Author(s): Tom Carter
139 Pages 
An Introduction to Information Theory and Applications
This note explains the following topics: uncertainty and information, Efficient coding of information, Stationary processes and markov chains, Coding for noisy transmission, Complements to efficient coding of Information, Error correcting codes and cryptography.
Author(s): F Bavaud, J C Chappelier and J Kohlas
293 Pages
This book explains basics of thermodynamics, including thermodynamic potentials, microcanonical and canonical distributions, and evolution in the phase space, The inevitability of irreversibility, basics of information theory, applications of information theory, new second law of thermodynamics and quantum information.
Author(s): Gregory Falkovich
165 Pages
Information Theory by Y. Polyanskiy
This PDF covers the following topics related to Information Theory : Information measures, Lossless data compression, Binary hypothesis testing, Channel coding, Lossy data compression, Advanced topics.
Author(s): Y. Polyanskiy, Y. Wu
295 Pages
Information Theory in Computer Science
This note explains the following topics: Shearer's Lemma, Entropy, Relative Entropy, Hypothesis testing, total variation distance and Pinsker's lemma, Stability in Shearer's Lemma, Communication Complexity, Set Disjointness, Direct Sum in Communication Complexity and Internal Information Complexity, Data Structure Lower Bounds via Communication Complexity, Algorithmic Lovasz Local Lemma, Parallel Repetition Theorem, Graph Entropy and Sorting.
Author(s): Madhu Sudan
NA Pages
Information Theory Lecture Notes
This is a graduate-level introduction to mathematics of information theory. This note will cover both classical and modern topics, including information entropy, lossless data compression, binary hypothesis testing, channel coding, and lossy data compression.
Author(s): Prof. Yury Polyanski
NA Pages
This note will explore the basic concepts of information theory. It is highly recommended for students planning to delve into the fields of communications, data compression, and statistical signal processing. Topics covered includes: Entropy and mutual information, Chain rules and inequalities, Data processing, Fano's inequality, Asymptotic equipartition property, Entropy rate, Source coding and Kraft inequality, Optimal code length and roof code, Huffman codes, Shannon-Fano-Elias and arithmetic codes, Maximum entropy, Channel capacity, Channel coding theorem, Differential entropy, Gaussian channel, Parallel Gaussian channel and water-filling, Quantization and rate-distortion.
Author(s): Yao Xie
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