Gatsby Computational
Neuroscience Unit

For a summary of the entire course you can read the following chapter:
Ghahramani (2004) Unsupervised Learning. In Bousquet, O., Raetsch, G. and von Luxburg, U. (eds) Advanced Lectures on Machine Learning LNAI 3176. SpringerVerlag.Code: COMP GI02 / COMP 4c51 / Gatsby
Year: MSc in Intelligent Systems, PhD course at the Gatsby Unit
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. Students wishing to take it for credit should consult with the course lecturer (email:
Term: 1, 2004
Time: 11.00 to 13.00 Mondays and Thursdays
Location: 4th floor, Gatsby Unit, 17 Queen Square
Taught By: Zoubin Ghahramani
Teaching Assistant: Katherine Heller.
Homework Assignments: all assignments (coursework) for this course 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 either Zoubin or Katherine. Late assignments will be penalised. If you are unable to come to class, you can also hand in assignments to Alexandra Boss, Room 408, Gatsby Unit.
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.
Textbook: There is no required textbook. However, I recommend the following recently published textbook as an excellent source for many of the topics here, and I will be occasionally assigning reading from it:
David J.C. MacKay (2003) Information Theory, Inference, and Learning Algorithms, Cambridge University Press. (also available online)This chapter summarises the entire course:
Ghahramani (2004) Unsupervised Learning. In Bousquet, O., Raetsch, G. and von Luxburg, U. (eds) Advanced Lectures on Machine Learning LNAI 3176. SpringerVerlag.
NOTE: If you want to see lecture slides from last year click on the 2003 course website, but be warned that the slides may change this year.
NOTE: NO LECTURE Mon OCT 4
Dates and Title  Topics  Materials 
Oct 7, Oct 11 Introduction and Statistical Foundations 

Lecture Slides Assignment 1 (due Oct 18) Suggested Further Readings:

Oct 14 and Oct 18 Latent Variable Models 

Lecture Slides Suggested Further Readings:

Oct 21 and 25 The EM Algorithm 

Lecture Slides Assignment 2 (due Nov 1) binarydigits.txt bindigit.m 
Oct 28 and Nov 1 Latent Variable Time Series Models 

Lecture Slides Suggested Further Readings:

Nov 4 Introduction to Graphical Models I 

Lecture Slides Assignment 3 (due Mon Nov 15) Data Sets: geyser.txt, data1.txt Suggested Further Readings: The following three related articles appear in Arbib (ed): The Handbook of Brain Theory and Neural Networks (2nd edition)

Nov 8 and 11 Reading Week 

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Nov 15 and 18 Introduction to Graphical Models II 

Assignment 4 (due Mon Nov 22) Belief Propagation Demo: Fluffy and Moby Factor Graph Propagation 
Nov 22 and 25 Hierarchical and Nonlinear Models 

Lecture Slides Demo Suggested Further Readings: Max Welling's Notes on ICA David MacKay's Book, Ch 34 on ICA 
Nov 29 and Dec 2 Sampling Methods 

Lecture Slides (MCMC) Suggested Further Readings: David MacKay's Book, Ch 29 and 30 on Monte Carlo methods; A more indepth treatment of Monte Carlo methods is in Radford Neal's Technical Report; 
Dec 6 Variational Approximations 

Lecture Slides (Variational) Assignment 5 (due Fri Dec 17) Data: images.jpg Code: genimages.m MStep.m
Suggested Further Readings:

Dec 9 Bayesian Model Comparison 

Lecture Slides (Bayesian Model Comparison)
Suggested Reading:

Aims: This course provides students with an indepth introduction to statistical modelling and unsupervised 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, 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 machine learning.
Learning Outcomes: To be able to understand the theory of unsupervised learning systems; to have indepth knowledge of the main models used in UL; to understand the methods of exact and approximate inference in probabilistic models; to be able to recognise which models are appropriate for different realworld applications of machine learning methods.
Method: Lecture presentations with associated class problems.
Assessment:
Course Location: 
Tel: Emails: 