COMP0080 Graphical Models

This database contains the 2018-19 versions of syllabuses. These are still being finalised and changes may occur before the start of the session.

Syllabuses from the 2017-18 session are available here.

Academic session

2018-19

Module

Graphical Models

Code

COMP0080

Module delivery

1819/A7P/T1COMP0080 Postgraduate

Related deliveries

1819/A7U/T1/COMP0080 Masters (MEng)

Prior deliveries

COMPGI08

Level

Postgraduate

FHEQ Level

L7

FHEQ credits

15

Term/s

Term 1

Module leader

Adamskiy, Dmitry

Contributors

Adamskiy, Dmitry

Module administrator

Adi, Abena

Aims

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

On successful completion of the module, a student will be able 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.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MRes Computational Statistics and Machine Learning
  • MRes Financial Computing
  • MRes Virtual Reality
  • MRes Web Science and Big Data Analytics
  • MSc Business Analytics (with specialisation in Computer Science)
  • MSc Computational Statistics and Machine Learning
  • MSc Data Science (International)
  • MSc Data Science and Machine Learning
  • MSc Machine Learning
  • MSc Web Science and Big Data Analytics
  • MRes Medical Physics and Biomedical Engineering
  • MSc Data Science

Prerequisites:

To be eligible to select this module, students must have:

  • understanding and abilities with Linear Algebra, Multivariate Calculus and Probability at mathematics FHEQ Level 4 (Undergraduate Year 1); and
  • familiarity with coding a high level language in order to complete assessments (strongly recommend that students are skilled in Python) (some tools in Matlab and Julia are provided)

Content

  • Bayesian Reasoning
  • Bayesian Networks
  • 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.

An indicative reading list is available via http://readinglists.ucl.ac.uk/departments/comps_eng.html.

Delivery

The module is delivered through a combination of lectures and self-directed learning.

Assessment

This module delivery is assessed as below:

#

Title

Weight (%)

Notes

1

Written examination (2hrs 30mins)

70

 

2

Coursework 1

10

LSA: Alternative oral assesment

3

Coursework 2

10

LSA: Alternative oral assesment

4

Coursework 3

10

LSA: Alternative oral assesment

In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.