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Machine Learning 4f13 Lent 2013

Keywords: Machine learning, probabilistic modelling, graphical models, approximate inference, Bayesian statistics

Taught By: Carl Edward Rasmussen and Zoubin Ghahramani

Code and Term: 4F13 Lent term

Year: 4th year (part IIB) Engineering, also open to MPhil and PhD students in any Department.

Structure & Assessment:14 lectures, 2 coursework revisions, 3 pieces of course work. The evaluation is by coursework only, all three pieces of course work carry an equal weight. There is no final exam.

Time: 11:00 - 12:00 Tuesdays and 10:00 - 11:00 Thursdays.

Location: Lecture Room 3 (LR3), Inglis Building, Trumpington Street, Cambridge

Prerequisites: A good background in statistics, calculus, linear algebra, and computer science. 3F3 Signal and Pattern Processing and 4F10 Statistical Pattern Processing would both be useful. You should thoroughly review the maths in the following cribsheet [pdf] [ps] before the start of the course. The following Matrix Cookbook is also a useful resource. If you want to do the optional coursework you need to know Matlab or Octave, or be willing to learn it on your own. Any student or researcher at Cambridge meeting these requirements is welcome to attend the lectures. Students wishing to take it for credit should consult with the course lecturers.

Textbook: There is no required textbook. However, the material covered is treated excellent recent text books:

Kevin P. Murphy Machine Learning: a Probabilistic Perspective, the MIT Press (2012).

David Barber Bayesian Reasoning and Machine Learning, Cambridge University Press (2012), avaiable freely on the web.

Christopher M. Bishop Pattern Recognition and Machine Learning. Springer (2006)

David J.C. MacKay Information Theory, Inference, and Learning Algorithms, Cambridge University Press (2003), available freely on the web.

NOTE: If you want to see lecture slides from a similar but not identical course taught last year click on the Lent 2012 course website, but be warned that the slides will change slightly this year.


LECTURE SYLLABUS

This year, the exposition of the material will be centered around three specific machine learning areas: 1) supervised non-paramtric probabilistic inference using Gaussian processes, 2) the TrueSkill ranking system and 3) the latent Dirichlet Allocation model for unsupervised learning in text.

Jan 17, 27 Introduction to Machine Learning(2L): probabilistic models, inference, Bayes rule Lecture 1 and 2 slides
Jan 24th, 29th and 31st Gaussian processes (3L): Lecture 3 and 4 slides
Lecture 5 slides
Feb 5th, 7th, 12th, 14th, 19th and 21st TrueSkill (4L):

Lecture 6 and 7: Introduction to Ranking
Feedback on Assignment number 1
Lecture 8 and 9: Message passing on graphs
Feb 26th, 28th, Mar 5th, 7th and 12th LDA (5L): Lecture 10: Discrete distributions
Lecture 11: Dirichelt distribtions and Text
Lecture 12: Graphical models for Text
Lecture 13 and 14: Latent Dirichlet Allocation for Topic Modelling

COURSE WORK

Course work is to be handed in to Laura Reed in Baker BNO-37 no later than 16:00 on the date due. Each of the three pieces of course work carry an equal weight in the evaluation.

Coursework #1
Coursework 1 is about regression using Gaussian processes. You will need the following files cw1d.mat, cw1e.mat and mauna.mat.
Due: 4pm Thursday Feb 14th, 2013 to Laura Reed, room BNO-37

Coursework #2
Coursework 2 will be about Probabilistic Ranking. You will need the following files cw/tennis_data.mat, cw/cw2.m, cw/gibbsrank.m and cw/eprank.m.
Due: 4pm Thursday February 28th, 2013 to Laura Reed, room BNO-37

Coursework #3
Coursework 3 is about the Latent Dirichlet Allocation (LDA) model. You will need the following files cw/kos_doc_data.mat, cw/bmm.m, cw/lda.m, cw/sampDiscrete.m.
Due: 4pm Thursday March 14th, 2013 to Laura Reed, room BNO-37