TY  - BOOK
AU  - MacKay, David J. C.
TI  - Information Theory, Inference and Learning Algorithms
SN  - 9780521642989
U1  - 003.54 
PY  - 2003///
CY  - UK
PB  - Cambridge University Press
KW  - Computer Engineering
N1  - Introduction to information theory
Probability, entropy and inference
More about inference
Part - 1: Data Compression:
The source coding theorem
Symbol codes
Stream codes
Codes for integers
Part - 2: Noisy-Channel Coding:
Dependent random variables
Communication over a noisy channel
The noisy-channel coding theorem
Error-correcting codes and real channels
Part - 3: Further Topics in Information Theory:
Hash codes
Binary codes
Very good linear codes exist
Further exercises on information theory
Message passing
Constrained noiseless channels
Crosswords and codebreaking
Why have sex? Information acquisition and evolution
Part - 4: Probabilities and Inference:
An example inference task: clustering
Exact inference by complete enumeration
Maximum likelihood and clustering
Useful probability distributions
Exact marginalization
Exact marginalization in trellises
Exact marginalization in graphs
Laplace's method
Model comparison and Occam's razor
Monte Carlo methods
Efficient Monte Carlo methods
Ising models
Exact Monte Carlo sampling
Variational methods
Independent component analysis
Random inference topics
Decision theory
Bayesian inference and sampling theory
Part - 5: Neural Networks:
Introduction to neural networks
The single neuron as a classifier
Capacity of a single neuron
Learning as inference
Hopfield networks
Boltzmann machines
Supervised learning in multilayer networks
Gaussian processes
Deconvolution
Part - 6: Sparse Graph Codes
Low-density parity-check codes
Convolutional codes and turbo codes
Repeat-accumulate codes
Digital fountain codes
Part - 7: Appendices: A. 
Some physics
Some mathematics
ER  -