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C. MacKay  \nInformation Theory, Inference, and Learning Algorithms  \nDavid J. C. MacKay  \n[mackay@mrao.cam.ac.uk](mackay@mrao.cam.ac.uk)  \n􀀍c1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003  \nVersion 6.0 (as published) June 26, 2003  \nPlease send feedback on this book via [http://www.inference.phy.cam.ac.uk/mackay/itila/](http://www.inference.phy.cam.ac.uk/mackay/itila/)  \nThis book will be [published by C.U.P. in](published by C.U.P. in) September 2003. It will remain viewable on-screen on the above website, in postscript, djvu, and pdf  \nformats.  \n(C. U.P. replace this page with their own page ii.)  \nContents  \nPreface ................................ v  \n1 Introduction to Information Theory ................ 3  \n2 Probability, Entropy, and Inference ................. 22  \n3 More about Inference ........................ 48  \nI Data Compression ........................ 65  \n4 The Source Coding Theorem .................... 67  \n5 Symbol Codes ............................ 91  \n6 Stream Codes ............................. 110  \n7 Codes for Integers .......................... 132  \nII Noisy-Channel Coding ...................... 137  \n8 Correlated Random Variables .................... 138  \n9 Communication over a Noisy Channel ............... 146  \n10 The Noisy-Channel Coding Theorem ................ 162  \n11 Error-Correcting Codes and Real Channels ............ 177  \nIII Further Topics in Information Theory ............. 191  \n12 Hash Codes: Codes for E􀀎cient Information Retrieval ..... 193  \n13 Binary Codes ............................ 206  \n14 Very Good Linear Codes Exist ................... 229  \n15 Further Exercises on Information Theory ............. 233  \n16 Message Passing ........................... 241  \n17 Communication over Constrained Noiseless Channels ...... 248  \n18 Crosswords and Codebreaking ................... 260  \n19 Why have Sex? Information Acquisition and Evolution ..... 269  \nIV Probabilities and Inference ................... 281  \n20 An Example Inference Task: Clustering .............. 284  \n21 Exact Inference by Complete Enumeration ............ 293  \n22 Maximum Likelihood and Clustering ................ 300  \n23 Useful Probability Distributions .................. 311  \n24 Exact Marginalization ........................ 319  \n25 Exact Marginalization in Trellises ................. 324  \n26 Exact Marginalization in Graphs .................. 334  \n27 Laplace's Method ........................... 341  \n28 Model Comparison and Occam's Razor .............. 343  \n29 Monte Carlo Methods ........................ 357  \n30 E􀀎cient Monte Carlo Methods ................... 387  \n31 Ising Models ............................. 400  \n32 Exact Monte Carlo Sampling .................... 413  \n33 Variational Methods ......................... 422  \n34 Independent Component Analysis and Latent Variable Modelling ................................. 437  \n35 Random Inference Topics ...................... 445  \n36 Decision Theory ........................... 451  \n37 Bayesian Inference and Sampling Theory ............. 457  \nV Neural networks ......................... 467  \n38 Introduction to Neural Networks .................. 468  \n39 The Single Neuron as a Classi􀀌er .................. 471  \n40 Capacity of a Single Neuron ..................... 483  \n41 Learning as Inference ........................ 492  \n42 Hop􀀌eld Networks .......................... 505  \n43 Boltzmann Machines ......................... 522  \n44 Supervised Learning in Multilayer Networks ............ 527  \n45 Gaussian Processes ......................... 535  \n46 Deconvolution ............................ 549  \nVI Sparse Graph Codes ...................... 555  \n47 Low-Density Parity-Check Codes ................. 557  \n48 Convolutional Codes and Turbo Codes ............... 574  \n49 Repeat{Accumulate Codes ..................... 582  \n50 Digital Fountain Codes ....................... 589  \nVII Appendic","cbCaie6UVWVboNRQ","https://ap.wps.com/l/cbCaie6UVWVboNRQ","pdf",11464312,640,"English","# Preface\n# Introduction to Information Theory\n# Probability, Entropy, and Inference\n# More about Inference\n## Data Compression\n## Noisy-Channel Coding\n## Further Topics in Information Theory\n## Probabilities and Inference\n## Neural networks\n## Sparse Graph Codes\n## Appendices","[{\"question\":\"Who is this book intended for?\",\"answer\":\"The book targets senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing, with prerequisites in calculus, probability theory, and linear algebra.\"},{\"question\":\"What central idea motivates the book’s structure?\",\"answer\":\"It unifies information theory and machine learning as two sides of the same coin, emphasizing shared tools across data compression, error-correcting codes, and learning algorithms.\"},{\"question\":\"How should readers use the book when deciding what to study first?\",\"answer\":\"Chapter dependencies are indicated in the figure on the next page, and advanced or optional topics within Parts I, II, IV, and V tend to appear toward the end; Part III chapters are optional on first reading.\"}]","Information Theory, Inference, and Learning Algorithms - Version 6.0 - June 26, 2003 | PDF",1789974044,1613]