COMPGI08 - Graphical Models

This database contains the 2016-17 versions of syllabuses. Syllabuses from the 2015-16 session are available here.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Code COMPGI08 (Also taught as: COMPM056 Graphical Models)
Year MSc
Term 1
Taught By David Barber (100%)
Aims The module provides an entry into probabilistic modeling and reasoning, primarily of discrete variable systems. Very little continuous variable calculus is required, and students more familiar with discrete mathematics should find the course digestible. The emphasis is to demonstrate the potential applications of the techniques in plausible real-world scenarios related to information retrieval and analysis. Concrete challenges include questionnaire analysis, low-density parity check error correction, and collaborative filtering of Netflix data.
Learning Outcomes Students will learn the basics of discrete graphical models, in particular inference algorithms in both singly and multiply connected structures. To cement understanding, the students must demonstrate their acquired skills by attacking several real-w