Informationtheory,inference,andlearninga.epub

Especially recommended [1] Simple (one minute) [2] Medium (quarter hour) Recommended [3] Moderately hard C Some parts require a computer [4] Hard [p. 42] Solution provided on page 42 [5] Research project Roadmaps The diagrams on the following pages will indicate the dependencies between chapters and a few possible routes through the book. c David J.C. MacKay. Draft 4.0. April 15, 2003 2 1 Introduction to Information Theory IV Probabilities and Inference 2 Probability, Entropy, and Inference 20 An Example Inference Task: Clustering 3 More about Inference 21 Exact Inference by Complete Enumeration 22 Maximum Likelihood and Clustering I Data Compression 23 Useful Probability Distributions 4 The Source Coding Theorem 24 Exact Marginalization 5 Symbol Codes 25 Exact Marginalization in Trellises 6 Stream Codes 26 Exact Marginalization in Graphs 7 An Aside: Codes for Integers 27 Laplace’s Method 28 Model Comparison and Occam’s Razor II Noisy-Channel Coding 29 Monte Carlo Methods 8 39 The Single Neuron as a Classifier 18 Crosswords and Codebreaking 40 Capacity of a Single Neuron 19 Wh......