Information Theory, Inference, and Learning Algorithms

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DS
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Joined: Tue Jan 06, 2004 6:17 am

Information Theory, Inference, and Learning Algorithms

Post by DS » Fri Sep 08, 2006 12:40 am

Феноменально популярная, по утверждению "Компьютерры", книжка кембриджского профессора, Дэвида Маккея "Information Theory, Inference, and Learning Algorithms" в свободном доступе at http://www.inference.phy.cam.ac.uk/mack ... /book.html

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Contents 
  1 Introduction to Information Theory 
  2 Probability, Entropy, and Inference 
  3 More about Inference 
Part I Data Compression 
  4 The Source Coding Theorem 
  5 Symbol Codes 
  6 Stream Codes 
  7 Codes for Integers  
Part II Noisy-Channel Coding 
  8 Dependent Random Variables 
  9 Communication over a Noisy Channel 
  10 The Noisy-Channel Coding Theorem 
  11 Error-Correcting Codes and Real Channels 
Part III  Further Topics in Information Theory 
  12 Hash Codes: Codes for Efficient Information Retrieval 
  13 Binary Codes  
  14 Very Good Linear Codes Exist 
  15 Further Exercises on Information Theory 
  16 Message Passing 
  17 Communication over Constrained Noiseless Channels  
  18 Crosswords and Codebreaking  
  19 Why have /S/e/x/? Information Acquisition and Evolution 
Part IV  Probabilities and Inference 
  20 An Example Inference Task: Clustering 
  21 Exact Inference by Complete Enumeration 
  22 Maximum Likelihood and Clustering 
  23 Useful Probability Distributions 
  24 Exact Marginalization 
  25 Exact Marginalization in Trellises 
  26 Exact Marginalization in Graphs 
  27 Laplace's Method 
  28 Model Comparison and Occam's Razor 
  29 Monte Carlo Methods 
  30 Efficient Monte Carlo Methods  
  31 Ising Models 
  32 Exact Monte Carlo Sampling  
  33 Variational Methods 
  34 Independent Component Analysis and Latent Variable Modelling  
  35 Random Inference Topics 
  36 Decision Theory 
  37 Bayesian Inference and Sampling Theory 
Part V  Neural networks 
  38 Introduction to Neural Networks 
  39 The Single Neuron as a Classifier 
  40 Capacity of a Single Neuron 
  41 Learning as Inference 
  42 Hopfield Networks 
  43 Boltzmann Machines 
  44 Supervised Learning in Multilayer Networks 
  45 Gaussian Processes  
  46 Deconvolution  
Part VI  Sparse Graph Codes  
  47 Low-Density Parity-Check Codes  
  48 Convolutional Codes and Turbo Codes 
  49 Repeat-Accumulate Codes 
  50 Digital Fountain Codes 
Part VII  Appendices 
   Notation; Some Physics; Some Mathematics 

Inex
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Joined: Mon Jan 05, 2004 10:33 am
Location: Санкт-Петербург

Post by Inex » Fri Sep 08, 2006 9:49 am

о! она там есть в djvu... здорово
а то она у меня была только в tex, из-за чего я ее до сих пор не читал... теперь точно с наладонника буду читать
кстати, на сходную, вроде, тему есть еще книжка:
Advanced learning theory, methods, applications (NATO, 2003)
"Когда Вы говорите, Иван Васильевич, у меня такое чувство, что Вы бредите"

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