We recommend viewing the videos online (synchronised with snapshots and slides) at the video lectures website.
Alternatively, the videos can be downloaded using the links below. We recommend using VLC to view them.
Lecture  Title  Date/Time  Videos  Snapshots  Slides 
Bonus  Counting (labelled unrooted) trees  06 Feb 2012, 16.00  00.f4v [ 46M]  00.pdf [6.1M]  00.html 
Lecture 1  Introduction to Information Theory  20 Feb 2012, 16.00  01.mp4 [675M]  01.pdf [ 16M]  01.html 
Lecture 2  Entropy and Data Compression (I): Introduction to Compression, Information Theory and Entropy 
27 Feb 2012, 14.30  02.mp4 [564M]  02.pdf [ 26M]  02.html 
Lecture 3  Entropy and Data Compression (II): Shannon's Source Coding Theorem, The Bent Coin Lottery 
05 Mar 2012, 14.30  03.mp4 [561M]  03.pdf [ 14M]  03.html 
Lecture 4  Entropy and Data Compression (III): Shannon's Source Coding Theorem, Symbol Codes 
16 Apr 2012, 14.30  04.mp4 [605M]  04.pdf [ 13M]  04.html 
Lecture 5  Entropy and Data Compression (IV): Shannon's Source Coding Theorem, Symbol Codes and Arithmetic Coding 
23 Apr 2012, 14.30  05.mp4 [670M]  05.pdf [ 26M]  05.html 
Lecture 6  Noisy Channel Coding (I): Inference and Information Measures for Noisy Channels 
30 Apr 2012, 14.30  06.mp4 [588M]  06.pdf [ 22M]  06.html 
Lecture 7  Noisy Channel Coding (II): The Capacity of a Noisy Channel 
07 May 2012, 14.30  07.mp4 [499M]  07.pdf [ 34M]  07.html 
Lecture 8  Noisy Channel Coding (III): The NoisyChannel Coding Theorem 
21 May 2012, 14.30  08.mp4 [745M]  08.pdf [ 28M]  08.html 
Lecture 9  A Noisy Channel Coding Gem, and An Introduction to Bayesian Inference (I) 
28 May 2012, 14.30  09.mp4 [535M]  09.pdf [ 46M]  09.html 
Lecture 10  An Introduction To Bayesian Inference (II): Inference Of Parameters and Models 
28 May 2012, 15.30  10.mp4 [825M]  10.pdf [ 43M]  10.html 

Approximating Probability Distributions (I): Clustering As An Example Inference Problem 
11 Jun 2012, 14.30  11.mp4 [629M]  11.pdf [ 27M]  11.html 

Approximating Probability Distributions (II): Monte Carlo Methods (I): Importance sampling, rejection sampling, Gibbs sampling, Metropolis method 
11 Jun 2012, 15.30  12.mp4 [908M]  12.pdf [ 51M]  12.html 
Lecture 13  Approximating Probability Distributions (III): Monte Carlo Methods (II): Slice sampling, Hybrid Monte Carlo, Overrelaxation, Exact Sampling 
25 Jun 2012, 14.30  13.mp4 [1.1G]  13.pdf [ 57M]  13.html 
Lecture 14  Approximating Probability Distributions (IV): Variational Methods 
09 Jul 2012, 14.30  14.mp4 [512M]  14.pdf [ 46M]  14.html 
Lecture 15  Data Modelling With Neural Networks (I): Feedforward Networks: The Capacity Of A Single Neuron, Learning As Inference 
09 Jul 2012, 15.30  15.mp4 [950M]  15.pdf [ 92M]  15.html 
Lecture 16  Data Modelling With Neural Networks (II): ContentAddressable Memories And StateOfTheArt ErrorCorrecting Codes 
16 Jul 2012, 14.30  16.mp4 [1.0G]  16.pdf [ 66M]  16.html 
Other course materials  free online text book [Information Theory, Inference, and Learning Algorithms]  software  further links  and errata
Our workflow, describing how the videos were recorded.