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 Noisy-Channel 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, Over-relaxation, 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): Content-Addressable Memories And State-Of-The-Art Error-Correcting 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.