COMPGI08 - Graphical Models
This database contains the 2017-18 versions of syllabuses. Syllabuses from the 2016-17 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)|
Excellent understanding and abilities with Linear Algebra, Multivariate Calculus and Probability all at first year undergraduate mathematics level.
Assignments will require students to have familiarity with coding a high level language; some tools in Matlab and Julia are provided.
|Taught By||Dmitry Adamsky [Teaching Fellow] (100%)|
David Barber [Module Lead]
To give an introduction to probabilistic modelling covering the broad theoretical landscape.
The course aims to cover much of the first 12 chapters of the course textbook www.cs.ucl.ac.uk/staff/d.barber/brml/
The emphasis is on probabilistic modelling of discrete variables.
|Learning Outcomes||Ability to construct probabilistic models, learn parameters and perform inference. This forms the foundation of many models in the wider sciences and students should be able to develop novel models for applications in a variety of related areas.|
Directed and Undirected Graphical Models
Inference in Singly-Connected Graphs
Hidden Markov Models
Junction Tree Algorithm
Decision Making under uncertainty
Markov Decision Processes
Learning with Missing Data
Approximate Inference using Sampling
If time permits we will also cover some deterministic approximate inference.
Method of Instruction
The course has the following assessment components:
- Written Examination (2.5 hours, 75%)
- Coursework (25%)
To pass this course, students must:
- Obtain an overall pass mark of 50% for all sections combined
Students will also be required to completed 8 weekly example problems from the course textbook.
Reading list available via the UCL Library catalogue.