The course is held on 3, 4 and 5 March from 3 pm to 7 pm (two lectures
per day).
The course is intended for Master's students and PhD students with some
mathematical background, as well as anyone else that is interested in
this topic.
The course outline is as follows:
Topic | Material | Excersizes |
Probability, Entropy and inference Balls and vases Statistical inference, Bayes' Rule Bent coin, legal evidence, Model Comparison |
MacKay Ch 2 MacKay Ch 3 Further reading: MacKay 30 |
Mackay 2.4,2.8,2.21,2.40,3.1 |
Exact inference
in graphs conditional independence Elimination, Probability propagation, Junction tree |
Jordan 2 (pg. 2-16) Sheets (pg. 18-25) MacKay 23.1 MacKay 28.1 Further reading: Jordan 3 Jordan 4 Jordan 17 | MacKay 23.2 MacKay 28.2 MacKay 28.5 |
Clustering, maximum likelihood estimation, EM |
MacKay 22 23.2 24 Jordan 11 | exercise EM |
Hidden Markov Models Multi-variate Gaussian |
Jordan 12 Jordan 13 | exercise HMM |
Factor Analysis, Kalman Filtering and smoothing |
Jordan 14 Jordan 15 | |
Linear perceptron Laplace's Method Monte Carlo methods Sheets |
MacKay 31 MacKay 42 MacKay 44 |
This lecture will be illustrated with a computer practical, using
Matlab.
To start this, download
this
file. Then do unzip mcmc (or let windows figure it out) which creates a directory mcmc with a number of matlab files in it. Do cd mcmc, and type matlab, and we are ready to go! In the same directory, you will find a file readme.ps that give further instructions and excersizes. |
*This course is described in the 'Doctorat en Informatica i comunicacio digital' as 'Aspectes de recuperacio de la informacio'.