

Unsupervised Learning (2006)
Dates

Mondays & Thursdays
2 October  14 December 2006

Time

11:0013:00

Lecturer

Maneesh Sahani

TA

Kai Krueger

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 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 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 it is optional. 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 in an 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)

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 either Maneesh or
Kai. Late assignments will be penalised. If you are unable to come
to class, 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.

