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. Please check the course descriptor. You should also thoroughly review the maths in the following cribsheet (from Iain Murray) before the start of the course, and attempt the tut0.pdf sheet. 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 Lecture Theatre 2, Appleton Tower on Tuesdays and Fridays 1000-1050.

Information taken from Informatics 2012/13 lecture timetable.

Resources

Assignments

There will be one assignment in this course, worth 20% of the overall mark for the course.
Handout date: 19 October, due date Fri 16 Nov (4pm). Feedback after 2 weeks (approx).

Tutorial groups

Tutorials will start in week 3.

Tutorial groups listing from ITO. This may only be visible from within .ed.ac.uk. In case of problems with the assignment to specific groups contact the ISS via the support form .

Course Rep

Sergey Dudoladov s1233200

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 2. It is probably best to catch me after the lecture. I will then walk over to my office in IF 2.27.

Examination Information

PMR will be examined in the December 2012 exam diet.

Week-by-Week listing

Will build up here ...

Week 1

Lectures Introduction slides slides4up, Belief networks slides slides4up
Self-check maths sheet Check if you can do the questions on tut0.pdf
Tutorials No tutorials in week 1 (nor week 2)

Week 2

Lectures Belief networks continued, Elimination algorithm slides, slides4up, Gaussian distribution slides, slides4up
Tutorials No tutorials in week 2, but see sheet below for the tutorial in week 3
Handout Worked example for Holmes/Watson network
JavaBayes example sprinkler.bif (Bayes net for the Holmes-Watson-Rain-Sprinkler problem)

Week 3

Lectures Gaussian distribution ctd, Maximum likelihood estimation slides, slides4up, Bayesian parameter estimation slides slides4up
Tutorials tut1.pdf, answer sheet ans1.pdf, wikipedia on Monty Hall problem
Handout on Inference with Gaussian Random Variables
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 4

NO LECTURE ON FRI 12 OCT
Lectures Decision theory slides, slides4up, Mixture models slides, slides4up
Handout Working for Gaussian classifier
Tutorials tut2.pdf, answer sheet ans2.pdf

Week 5

Lectures Mixture models continued, Factor analysis and beyond slides, slides4up
Handout Working for EM for Mixture of Gaussians
Handout Working for PCA solution as principal eigenvector
Handout on Factor Analysis and Beyond
Tutorials tut3_1213.pdf, matlab code plotquad.m, answer sheet ans3_1213.pdf

Week 6

Lectures Factor analysis and beyond ctd, Bayesian Model Selection slides, slides4up, Hidden Markov models slides slides4up
Tutorials tut4_1213.pdf, answer sheet ans4_1213.pdf
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)
Web resource chapter 28 from David MacKay's book Information Theory, Inference and Learning Algorithms. See sections 28.1, 28.2 on model comparison

Week 7

Lectures Hidden Markov Models continued, Time Series Modelling and Kalman Filters slides, slides4up
Tutorials tut5_1213.pdf, answer sheet ans5_1213.pdf
Handout Working for alpha and beta recursions for HMMs
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.
Handout on Time Series Modelling
Web resource Movie clips from Prof Andrew Blake's group illustrating tracking with non-linear Kalman filters

Week 8

Lectures Time Series Modelling and Kalman Filters ctd, Junction tree algorithm slides slides4up
Tutorials tut6_1112.pdf, answer sheet ans6_1112.pdf
Handout Worked example of inference in a junction tree
Handout Worked example for c->b->a network

Week 9

NO LECTURE ON FRIDAY 16 NOV
Lectures Undirected graphical models slides, slides4up
Tutorials tut7_1112.pdf, answer sheet ans7_1112.pdf
Handouts Working for local Markov property, Working for Boltzmann machine conditional distribution, Working for derivative of a log-linear model
Web resource Information about GrabCut from MSR Cambridge

Week 10

Lectures Last lecture on Tues 20 Nov.
Finish off Undirected graphical models, followed by question and answer session. If there is time I will then discuss Coding and Information Theory (not examinable) slides, slides4up
Tutorials tut8_1112.pdf, answer sheet ans8_1112.pdf

Revision Session

I will hold a revision session on Thurs 6 Dec 10-11am in the Hugh Robson Building Lecture Theatre. This will be a question and answer format, not a lecture.

This page is maintained by Chris Williams




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