Course on Information Theory, Pattern Recognition, and Neural Networks

Course on Information Theory, Pattern Recognition, and Neural Networks

16 Lectures · Feb 20, 2012

About

A series of sixteen lectures covering the core of the book [[http://www.inference.phy.cam.ac.uk/mackay/itila/| "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 .

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Uploaded videos:

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01:01:51

Lecture 1: Introduction to Information Theory

David MacKay

Nov 05, 2012

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145837 Views

Lecture
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51:08

Lecture 2: Entropy and Data Compression (I): Introduction to Compression, Infor...

David MacKay

Nov 05, 2012

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36522 Views

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51:00

Lecture 3: Entropy and Data Compression (II): Shannon's Source Coding Theorem an...

David MacKay

Nov 05, 2012

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22391 Views

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56:56

Lecture 4: Entropy and Data Compression (III): Shannon's Source Coding Theorem, ...

David MacKay

Nov 05, 2012

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19756 Views

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01:02:47

Lecture 5: Entropy and Data Compression (IV): Shannon's Source Coding Theorem, S...

David MacKay

Nov 05, 2012

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17832 Views

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54:41

Lecture 6: Noisy Channel Coding (I): Inference and Information Measures for Nois...

David MacKay

Nov 05, 2012

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16486 Views

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46:53

Lecture 7: Noisy Channel Coding (II): The Capacity of a Noisy Channel

David MacKay

Nov 05, 2012

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13909 Views

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01:08:58

Lecture 8: Noisy Channel Coding (III): The Noisy-Channel Coding Theorem

David MacKay

Nov 05, 2012

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14425 Views

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48:35

Lecture 9: A Noisy Channel Coding Gem, And An Introduction To Bayesian Inference...

David MacKay

Nov 05, 2012

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14922 Views

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01:15:52

Lecture 10: An Introduction To Bayesian Inference (II): Inference Of Parameters ...

David MacKay

Nov 05, 2012

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21236 Views

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56:55

Lecture 11: Approximating Probability Distributions (I): Clustering As An Exampl...

David MacKay

Nov 05, 2012

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13518 Views

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01:23:47

Lecture 12: Approximating Probability Distributions (II): Monte Carlo Methods (I...

David MacKay

Nov 05, 2012

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21040 Views

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01:47:56

Lecture 13: Approximating Probability Distributions (III): Monte Carlo Methods (...

David MacKay

Nov 05, 2012

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13817 Views

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46:34

Lecture 14: Approximating Probability Distributions (IV): Variational Methods

David MacKay

Nov 05, 2012

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14782 Views

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01:27:15

Lecture 15: Data Modelling With Neural Networks (I): Feedforward Networks: The C...

David MacKay

Nov 05, 2012

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15749 Views

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01:36:32

Lecture 16: Data Modelling With Neural Networks (II): Content-Addressable Memori...

David MacKay

Nov 05, 2012

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13334 Views

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