**Taught By: **
Carl Edward Rasmussen
and David Krueger

**Code and Term:** 4F13 Michaelmas term

**Year:** 4th year (part IIB) Engineering and MPhil in Machine
Learning and Machine Intelligence; the lectures are also open to students
in any department (but if you want to take it for credit, you need to make
arrangements for assessment within your own department, as our capacity
to mark coursework is already severely stretched).

**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.

**Format:**This year the course will be delivered entirely
online. The content will be a combination of pre-recorded lectures
and live on-line Q&A session via zoom. The Q&A sessions will
take place in the time tabled slots for the course which are Mondays
at 9:00 - 10:00 and Tuesdays 9:00 - 10:00, first time Monday October
11th. You should have watched the lecture material before coming to
the Q&A. Lecture material will be published on the course moodle
site at the latest on Thursday morning in the week prior to the
Q&A session. Each lecture generally comprises 3 short videos of
about 15-20 minute duration.

**Prerequisites: **A good background in statistics, calculus, linear
algebra, and computer science. 3F3 Signal and Pattern Processing. 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.

Course work is to be submitted via moodle
in electronic form no later than 12:00 noon on the date due. If you are
**not** an egineering undergraduate, please make sure you are
signed up for the module on moodle, check with Kimberly Cole
`kc429@cam.ac.uk`, in room BE4-45 if you are in doubt. 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:

Coursework 1 is about regression using Gaussian processes. You will need the following files cw1a.mat and cw1e.mat.

Coursework 2 will be about Probabilistic Ranking. This is the data file: tennis_data.mat. For matlab, use cw2.m, gibbsrank.m and eprank.m, or for python use coursework2.ipynb, cw2.py, gibbsrank.py and eprank.py.

Coursework 3 is about the Latent Dirichlet Allocation (LDA) model. You will need the kos_doc_data.mat, and code for matlab bmm.m, lda.m, sampDiscrete.m, or code for python bmm.py, lda.py, sampleDiscrete.py.