Probabilistic Modelling and Reasoning
Course homepage
This is a course for MSc level students. The course descriptor can
be found
here.
The
introductory
handout for the course is available.
MATHS PREREQUSITES: To do PMR you need a
reasonable background in statistics, calculus, linear algebra.
You should thoroughly review the maths in the following
cribsheet (from Iain Murray, UCL)
before the start of the course.
A handout on
Mathematical
Preliminaries which used to be used for the course LfD1 is also
useful for PMR; all material is relevant.
Lectures
PMR lectures will be in Old College 270 on Tuesdays at 1000-1050 and
Fridays at 1000-1050.
A map of the
Law Buildings/section of Old College is available, see pages 2 and
3. Note that there is an entrance to Old College in the SW corner,
near to the pedestrian underpass.
Resources
-
Books. The course textbook is Pattern
Recognition and Machine Learning by Christopher M. Bishop,
Springer (2006).
- Other useful texts for some of the material are
Information Theory, Inference and Learning Algorithms by
David J. C. MacKay (Cambridge University Press, 2003), and
Artificial
Intelligence: A Modern Approach by S. Russell and P. Norvig (Prentice Hall,
second edition, 2002).
-
A web
page giving details of some books with maths background,
and some web-based resources
-
Links
to more advanced mathematical material.
-
A handout Introduction
to MATLAB giving an introduction to MATLAB and the NETLAB neural networks
toolbox. Further MATLAB tutorials are available at
UNH
Matlab Tutorial, US
Navy Matlab Tutorial and MTU
Introduction to Matlab
-
A Brief
Introduction to Graphical Models and Bayesian Networks by Kevin Murphy.
-
Thomas Minka's excellent Statistical
Learning/Pattern Recognition Glossary
-
Max Welling's Classnotes in Machine Learning
-
The Association for Uncertainty in Artificial
Intelligence homepage is a good place to start looking for interesting
material.
-
The Kalman Filter
website.
-
List
of Public Domain Belief Network Tools from a course on Belief Networks
at Duke University.
- Zoubin Ghahramani's tutorial/overview paper
Unsupervised Learning (original
link), in Bousquet, O., Raetsch, G. and von Luxburg, U. (eds) Advanced Lectures on Machine Learning LNAI 3176, Springer-Verlag (2004).
Assignments
There will be two assignments in this module worth in total 30%
of the mark for the course.
Tutorial groups
Tutorials will start in week 3.
Allocations can be found
here
Course Rep
Jonathan Millin s0967420
Software
We will be using the program JavaBayes, you can find more details
here. We will also be using some MATLAB code.
Office hour
Friday 11-12, starting in week 4 (but not week 8).
It is probably best to catch me
after the lecture. I will then walk over to my office (IF 2.27).
Week-by-Week listing
Week 1
Lectures
Introduction
slides
slides4up
Tutorials No tutorials in week 1 (nor week 2)
Week 2
Lectures Belief networks
slides
slides4up
Tutorials No tutorials in week 2, but see sheet below for
the tutorial in week 3
JavaBayes example
sprinkler.bif (Bayes net for the
Holmes-Watson-Rain-Sprinkler problem)
Week 3
Lectures Elimination algorithm
slides,
slides4up,
Junction tree algorithm
slides
slides4up
Handout Worked example of
inference in a junction tree
Tutorials tut1.pdf,
answer sheet
ans1.pdf,
wikipedia
on Monty Hall problem
Week 4
Lectures
Gaussian distribution
slides,
slides4up,
Maximum likelihood estimation
slides,
slides4up
Handout on
Inference
with Gaussian Random Variables
Tutorials tut2.pdf,
answer sheet
ans2.pdf
Week 5
Lectures
Bayesian parameter estimation
slides
slides4up,
Decision theory
slides,
slides4up
Tutorials tut3.pdf,
answer sheet
ans3.pdf
Matlab code cointoss.m,
matlab code to illustrate posterior distribution under Beta prior
Web resource The
technical
report by David Heckerman entitled "A tutorial on Learning Bayesian
Networks". (
original
link)
Week 6
Lectures
Mixture models
slides,
slides4up ,
Factor analysis and beyond
slides,
slides4up,
Tutorials tut4.pdf, matlab code
plotquad.m,
answer sheet
ans4.pdf
Handout on
Factor Analysis and Beyond
Web resource Te-Won
Lee's Blind source separation demo
Web resource Short explanation
of blind source separation from Helsinki
Optional reading
Paper on
GTM
by C. M. Bishop, M. Svensen and C. K. I. Williams (
original
link)
Week 7
Lectures
Hidden Markov models
slides
slides4up
Tutorials tut5.pdf,
answer sheet
ans5.pdf
Web resource L Rabiner tutorial on HMMs from Proceedings of the
IEEE 77(2) 1989 is available from the IEL electonic library
here.
Optional reading
A Gentle Tutorial on the EM Algorithm and its Application to Parameter
Estimation for Gaussian Mixture and Hidden Markov Models by Jeff A. Bilmes
pdf
(
original
link)
Web resource
Harmonising chorales in the style of Johann Sebastian Bach, work
by Moray Allan using a HMM (MSc, School of Informatics, Edinburgh,
2002). See also
HMM Bach demo.
Web resource Movie
clips from Prof Andrew Blake's group illustrating tracking with non-linear
Kalman filters
Week 8
Lectures
Bayesian Model Selection
slides,
slides4up,
Coding and Information Theory
slides,
slides4up
Tutorials tut6.pdf,
answer sheet
ans6.pdf
Week 9
Lectures
Undirected graphical models
slides,
slides4up
Tutorials tut7.pdf,
answer sheet
ans7.pdf
Week 10
No lectures in week 10
Tutorials tut8.pdf,
answer sheet
ans8.pdf
This page is maintained by Chris
Williams
