**Taught By: **
Carl Edward Rasmussen

**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 assessment **depends on which cohort you
are in**. For undergrad students and PhD students the assessment will be
based on you handing in reports for each of the three pieces of
coursework (and there will be no final exam); the three pieces of coursework
carry equal weight. For students in the Machine
Learning and Machine Intelligence (MLMI) MPhil program (the MLMI module
number is MLMI17), the assessment will be by a short oral exam, which
will be held on Friday December 6th, somehwere in the interval 8:00
- 17:00. The MLMI students don't hand in written reports, but should
work on these questions in preparation for the oral. More information
on the exact format of the oral to follow.

**Format:**This year the course will be taught in person, in LT
1, weekly on Mondays 9:00-10:00 and Tuesdays 9:00-10:00, the first
lecture on Monday Oct 14th. There will also be an (entirely optional)
office hour on Thursdays 15:00-16:00 (first time Oct 17th) in the CBL
seminar room BE4-38 (4th floor Baker Building).

**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 updated about two weeks before it is due,
coursework 1 is up-to-date. 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.