

Probabilistic & Unsupervised Learning (2007)
Dates

Mondays & Thursdays
1 October  13 December 2007

Time

11:0013:00

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 indepth 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,
SpringerVerlag.
Specific recommendations for reading are available here.

Lecture schedule



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.


To attend

Please contact
the unit.

