Probabilistic & Unsupervised Learning (2007)

Dates Mondays & Thursdays
1 October - 13 December 2007
Time 11:00-13:00
Lecturers Maneesh Sahani and Yee Whye Teh
TA Debjyoti Ray
Location 4th Floor Seminar Room, Alexandra House, 17 Queen Square, London [directions]
About the course

This course provides students with an in-depth introduction to statistical modelling, unsupervised, and some supervised learning techniques. It presents probabilistic approaches to modelling and their relation to coding theory and Bayesian statistics. A variety of latent variable models will be covered including mixture models (used for clustering), dimensionality reduction methods, time series models such as hidden Markov models which are used in speech recognition and bioinformatics, Gaussian process models, independent components analysis, hierarchical models, and nonlinear models. The course will present the foundations of probabilistic graphical models (e.g. Bayesian networks and Markov networks) as an overarching framework for unsupervised modelling. We will cover Markov chain Monte Carlo sampling methods and variational approximations for inference. Time permitting, students will also learn about other topics in probabilistic (or Bayesian) machine learning.

The course is run primarily for new Gatsby students for whom it is mandatory, and for students on the CS MSc in Intelligent Systems, for whom only the first 6 weeks are required. Students, postdocs and faculty from outside the unit and this programme are welcome to attend, but should contact the unit in advance.

Prerequisites

A good background in statistics, calculus, linear algebra, and computer science. You should thoroughly review the maths in the following cribsheet [pdf] [ps] before the start of the course. You must either know Matlab or Octave, be taking a class on Matlab/Octave, or be willing to learn it on your own. Any student or researcher at UCL meeting these requirements is welcome to attend the lectures.

Text

There is no required textbook. However, the following are excellent sources for many of the topics covered here.

David J.C. MacKay (2003) Information Theory, Inference, and Learning Algorithms, Cambridge University Press. (also available online)
Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer-Verlag.
Specific recommendations for reading are available here.
Lecture schedule
October
1/10: Introduction 4/10: Statistical Foundations
8/10: Latent Variable Models 11/10: continued
15/10: The EM algorithm 18/10: continued
22/10: Latent time-series models 25/10: continued
29/10: Introduction to graphical models    
 
November
    1/11: continued
5/11: cancelled 8/11: Bayesian Treatments of Probabilistic Models
End of material required for IS MSc students
12/11: Hierarchical and Nonlinear Models 15/11: continued
19/11: Sampling and Monte Carlo 22/11: continued
26/11: Variational approximations 29/11: continued
 
December
3/12: no lecture 6/12: no lecture
12/12: Expectation Propagation 13/12: Final wrapup
 
Assignments
50% of the total mark is based on coursework assignments (the other 50% being based on the final written examination).

All assignments (coursework) are to be handed in to the Gatsby Unit, not to the CS department. Please hand in all assignments at the beginning of lecture on the due date to the lecturer or to Deb. Late assignments will be penalised. If you are unable to attend the lecture, you can also hand in assignments to Ms. Rachel Howes at the Alexandra House 4th floor reception.

Late Assignment Policy: Assignments that are handed in late will be penalised as follows: 10% penalty per day for every weekday late, until the answers are discussed in a review session. NO CREDIT will be given for assignments that are handed in after answers are discussed in the review session.

Assignment 1        [binarydigits.txt, bindigit.m] due 18 Oct 2007
Assignment 2        [geyser.txt, data3d.txt] due 1 Nov 2007
Assignment 3        [co2.txt] due 19 Nov 2007
Assignment 4        [code.tgz] due 3 Dec 2007
Assignment 5        [images.jpg, genimages.m, Mstep.m] due 14 Dec 2007

 
To attend Please contact the unit.