David MacKay
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01:23:47
Lecture 12: Approximating Probability Distributions (II): Monte Carlo Methods (I...
Nov 5, 2012 21263 views
01:36:32
Lecture 16: Data Modelling With Neural Networks (II): Content-Addressable Memori...
Nov 5, 2012 13452 views
46:34
Lecture 14: Approximating Probability Distributions (IV): Variational Methods
Nov 5, 2012 14919 views
01:02:47
Lecture 5: Entropy and Data Compression (IV): Shannon's Source Coding Theorem, S...
Nov 5, 2012 18081 views
54:41
Lecture 6: Noisy Channel Coding (I): Inference and Information Measures for Nois...
Nov 5, 2012 16776 views
46:53
Lecture 7: Noisy Channel Coding (II): The Capacity of a Noisy Channel
Nov 5, 2012 14072 views
48:35
Lecture 9: A Noisy Channel Coding Gem, And An Introduction To Bayesian Inference...
Nov 5, 2012 15093 views
01:15:52
Lecture 10: An Introduction To Bayesian Inference (II): Inference Of Parameters ...
Nov 5, 2012 21462 views
51:00
Lecture 3: Entropy and Data Compression (II): Shannon's Source Coding Theorem an...
Nov 5, 2012 22634 views
56:56
Lecture 4: Entropy and Data Compression (III): Shannon's Source Coding Theorem, ...
Nov 5, 2012 19962 views
01:08:58
Lecture 8: Noisy Channel Coding (III): The Noisy-Channel Coding Theorem
Nov 5, 2012 14625 views
01:27:15
Lecture 15: Data Modelling With Neural Networks (I): Feedforward Networks: The C...
Nov 5, 2012 15887 views