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University of Cambridge Home Department of Engineering

Machine Learning 4f13 Lent 2015

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: 9:00 - 10:00 Mondays and 9:00 - 10:00 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 2014 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-parametric probabilistic inference using Gaussian processes, 2) the TrueSkill ranking system and 3) the latent Dirichlet Allocation model for unsupervised learning in text.

Jan 15, 19 Introduction to Machine Learning (2L):
the concept of a model, linear in the parameters regression: making predictions, least squares fit, overfitting
likelihood and the concept of noise: Gaussian iid noise, maximum likelihood fitting, equivalence to least squares, motivation for inference with multiple hypotheses
probability basics: Medical diagnosis example, joint, conditional and marginal probability, the two rules: sum and product, and Bayes rule
Bayesian inference and prediction: likelihood and prior, posterior and predictive distribution
Marginal Likelihood: Bayesian model selection, example: How Bayes avoids overfitting
Lecture 1 and 2 slides
Jan 22, 26, 29 Gaussian Processes (3L):
Distributions over parameters and over functions: Motivation: representation of multiple hypothesis, concepts of prior over functions and over parameters, inference, priors over functions are priors over long vectors
Gaussian process priors: from finite multi-variate Gaussians to Gaussian processes, GP definition, conditional generation and joint generation
Lecture 3 and 4 slides
Lecture 5 slides
Feb 2, 5, 9 and 12 Probabilistic Ranking (4L): Lecture 6 and 7 slides
Lecture 8 and 9 slides
Feb 19, 23, 26 Text and Discrete Distributions:
Motivation: unsupervised learning on text corpora, discrete distributions, Bernoulli, Binomial, multinomial, categorical and Dirichlet distributions
Mixture models for text and the Expectation Maximization (EM) algorithm.
Latent Dirichlet Allocation (LDA) model
Lecture 10 and 11 slides
Lecture 12 slides
Lecture 13 and 14 slides

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. The course work will be similar, but not identical to last year's, and will be posted shortly on this web site. The due-dates this year are: February 3rd, February 24th and March 13th.

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 February 3rd, 2015 to Laura Reed, room BNO-37

Coursework #2
Coursework 2 will be about Probabilistic Ranking. You will need the following files tennis_data.mat, cw2.m, gibbsrank.m and eprank.m.
Due: 4pm February 24th, 2015 to Laura Reed, room BNO-37

Coursework #3
Coursework 3 is about the Latent Dirichlet Allocation (LDA) model. You will need the following files kos_doc_data.mat, bmm.m, lda.m, sampDiscrete.m.
Due: 4pm March 13th, 2015 to Laura Reed, room BNO-37