Course pages 2019–20

# Probabilistic Machine Learning

**Principal lecturer:** Prof Carl Rasmussen**Additional lecturer:** Dr Damon Wischik**Taken by:** MPhil ACS, Part III**Code:** LE49**Hours:** 18 (14 lectures at the Department of Engineering, 2 examples classes; Lectures for this module are offered by the Department of Engineering, Trumpington Street site, during Michaelmas Term, Mondays 9 a.m. and Thursdays at 12. Practical work in Lent term is administered and assessed by the Department of Computer Science and Technology.)**Class limit:** 12 students**Prerequisites:** Strong background in statistics, calculus and linear algebra, courses in statistical signal processing will be required. Note that the lectures in this course are primarily oriented toward Cambridge Engineering students. More detail of prerequisites should be reviewed on the Engineering Department website: http://mlg.eng.cam.ac.uk/teaching/4f13/1819/ .
Students are asked to talk with their Course Adviser before selecting this module.

## Further information

This module is borrowed from the Department of Engineering and the lectures are given at the Trumpington Street site during Michaelmas Term. Students wishing to take this module should note that the Department of Engineering is about 2 miles from the Computer Laboratory.

## Syllabus

Please refer to the Engineering Course 4f13 webpage for details of the content.

## Assessment

There are four pieces of coursework. The first three are structured exercises designed to reinforce the lectures. The fourth is an open-ended investigation of a topic that you chose from a small list, drawing on the main themes of the lecture course.

- Gaussian processes exercise (10%, due in Michaelmas term)
- Probabilistic ranking exercise (10%, due in Michaelmas term)
- Topic modelling exercise (10%, due in Michaelmas term)
- Investigative project (70%, due beginning of Lent term)

The investigative project should be written up in the style of a NIPS or ICML conference paper, 8 pages plus one for references.

Online submission is available from the **Moodle** page *(Only available to Cambridge University staff and students)*