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original:http://videolectures.net/course_information_theory_pattern_recognition/

Course on Information Theory, Pattern Recognition, and Neural Networks

A series of sixteen lectures covering the core of the book “Information Theory, Inference, and Learning Algorithms (Cambridge University Press, 2003)” which can be bought at Amazon, and is available free online. A subset of these lectures used to constitute a Part III Physics course at the University of Cambridge. The high-resolution videos and all other course material can be downloaded from the Cambridge course website.

[syn]  3480 views, 1:01:50   Lecture 1: Introduction to Information Theory David MacKay 1 comment	[syn]  638 views, 51:08   Lecture 2: Entropy and Data Compression (I): Introduction to Compression, Information Theory and Entropy David MacKay	[syn]  299 views, 51:00   Lecture 3: Entropy and Data Compression (II): Shannon’s Source Coding Theorem and the Bent Coin Lottery David MacKay	[syn]  235 views, 56:57   Lecture 4: Entropy and Data Compression (III): Shannon’s Source Coding Theorem, Symbol Codes David MacKay	[syn]  188 views, 1:02:47   Lecture 5: Entropy and Data Compression (IV): Shannon’s Source Coding Theorem, Symbol Codes and Arithmetic Coding David MacKay
[syn]  168 views, 54:41   Lecture 6: Noisy Channel Coding (I): Inference and Information Measures for Noisy Channels David MacKay 1 comment	[syn]  136 views, 46:53   Lecture 7: Noisy Channel Coding (II): The Capacity of a Noisy Channel David MacKay	[syn]  138 views, 1:08:58   Lecture 8: Noisy Channel Coding (III): The Noisy-Channel Coding Theorem David MacKay	[syn]  187 views, 48:35   Lecture 9: A Noisy Channel Coding Gem, And An Introduction To Bayesian Inference (I) David MacKay	[syn]  414 views, 1:15:52   Lecture 10: An Introduction To Bayesian Inference (II): Inference Of Parameters And Models David MacKay
[syn]  191 views, 56:55   Lecture 11: Approximating Probability Distributions (I): Clustering As An Example Inference Problem David MacKay	[syn]  231 views, 1:23:47   Lecture 12: Approximating Probability Distributions (II): Monte Carlo Methods (I): Importance Sampling, Rejection Sampling, Gibbs Sampling, Metropolis Method David MacKay	[syn]  170 views, 1:47:56   Lecture 13: Approximating Probability Distributions (III): Monte Carlo Methods (II): Slice Sampling, Hybrid Monte Carlo, Over-relaxation, Exact Sampling David MacKay	[syn]  218 views, 46:34   Lecture 14: Approximating Probability Distributions (IV): Variational Methods David MacKay	[syn]  269 views, 1:27:15   Lecture 15: Data Modelling With Neural Networks (I): Feedforward Networks: The Capacity Of A Single Neuron, Learning As Inference David MacKay
[syn]  166 views, 1:36:32   Lecture 16: Data Modelling With Neural Networks (II): Content-Addressable Memories And State-Of-The-Art Error-Correcting Codes David MacKay