Lecture Notes On Information Theory
These notes provide a graduate-level introduction to the mathematics of Information Theory. Topics covered includes: Information measures: entropy, divergence and mutual information, Sufficient statistic, Extremization of mutual information, Lossless data compression, Channel coding, Linear codes, Lossy data compression, Applications to statistical decision theory, Multiple-access channel, Entropy method in combinatorics and geometry.
Author(s): Yihong Wu
342 Pages 
Information Theory Inference and Learning Algorithms
This book contains below topics, Introduction to information theory, Probability, entropy, and inference, Source coding, Noisy channel coding, Sparse graph codes, Bayesian inference and Monte Carlo methods, Neural networks, Decision theory.
Author(s): David J. C. MacKay, University of Cambridge
640 Pages
Applied Digital Information theory
This note serves as a comprehensive guide to fundamental concepts in information theory and coding. This pdf provides discrete probability theory, information theory, and coding principles. Beginning with Shannons measure of information, then delves into the efficient coding of information, the methodology of typical sequences is introduced, emphasizing the distinction between lossy and lossless source encoding. The text also discusses coding for noisy digital channels, block coding principles and tree and trellis coding principles.
Author(s): James L.Massey
153 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 Lecture Notes
This PDF covers the following topics related to Information Theory, Introduction, Entropy, Relative Entropy, and Mutual Information, Asymptotic Equipartition Properties, Communication and Channel Capacity, Method of Types, Conditional and Joint Typicality, Lossy Compression and Rate Distortion Theory, Joint Source Channel Coding.
Author(s): Prof. Tsachy Weissman
75 Pages
Information Theory and Coding cam
The PDF covers the following topics related to Information Theory, Foundations, probability, uncertainty, information, Entropies defined, and why they are measures of information, Source coding theorem, prefix, variable, and fixedlength codes, Channel types, properties, noise, and channel capacity, Continuous information, density, noisy channel coding theorem, Fourier series, convergence, orthogonal representation, Useful Fourier theorems, transform pairs, Sampling, aliasing, Discrete Fourier transform, Fast Fourier Transform Algorithms, The quantised degreesoffreedom in a continuous signal, GaborHeisenbergWeyl uncertainty relation, Kolmogorov complexity.
Author(s): J G Daugman
75 Pages
Information Theory and its applications in theory of computation
This note covers the following topics, Entropy, Krafts inequality, Source coding theorem, conditional entropy, mutual information, KLdivergence and connections, KLdivergence and Chernoff bounds, Data processing and Fanos inequalities, Asymptotic Equipartition Property, Universal source coding, LempelZiv algorithm and proof of its optimality, Source coding via typical sets and universality, joint typicality and joint AEP, discrete channels and channel capacity, Proof of Noisy channel coding theorem, Constructing capacityachieving codes via concatenation, Polarization, Arikans recursive construction of a polarizing invertible transformation, Polar codes construction, Bregmans theorem, Shearers Lemma and applications, Source coding and Graph entropy, Monotone formula lower bounds via graph entropy, Optimal set Disjointness lower bound and applications, Compression of arbitrary communication protocols, Parallel repetition of 2prover 1round games.
Author(s): Venkatesan Guruswami and Mahdi Cheraghchi
NA Pages
