Program Overview

Graphical Models (COMP0080)

Key Information

The module is part of the Faculty of Engineering Sciences, specifically the Computer Science department, and is worth 15 credits.

• Faculty: Faculty of Engineering Sciences
• Teaching department: Computer Science
• Credit value: 15

Restrictions

The module has specific restrictions for different levels of study:

• Module delivery for UG Masters (FHEQ Level 7) is available on MEng Computer Science; MEng Mathematical Computation.
• Module delivery for PGT (FHEQ Level 7) is available on MSc Computational Statistics and Machine Learning; MSc Data Science and Machine Learning; MSc Machine Learning; MSc Data Science; MSc Scientific and Data Intensive Computing; MRes Artificial Intelligence Enabled Healthcare; MRes Medical Imaging.

Alternative Credit Options

There are no alternative credit options available for this module.

Description

Aims

The module introduces probabilistic modelling, covering the broad theoretical landscape, with an emphasis on probabilistic modelling of discrete variables.

Intended Learning Outcomes

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

1. 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.

Indicative Content

The module will typically cover the following topics:

• 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, some deterministic approximate inference may also be covered.

Requisites

To be eligible to select this module as an optional or elective, a student must:

1. Be registered on a programme and year of study for which it is formally available;
2. Have an understanding of and abilities with Linear Algebra, Multivariate Calculus, and Probability at mathematics FHEQ Level 4 or above;
3. Have familiarity with coding in a high-level language in order to complete assessments (strongly recommend that students are skilled in Python), with some tools provided in Matlab and Julia.

Module Deliveries for 2026/27 Academic Year

Intended Teaching Term: Term 1, Postgraduate (FHEQ Level 7)

Teaching and Assessment

• Mode of study: In person
• Methods of assessment: 70% Exam, 30% Coursework
• Mark scheme: Numeric Marks

Other Information

• Number of students on module in previous year: 40
• Module leader: Dr Dmitry Adamskiy

Intended Teaching Term: Term 1, Undergraduate (FHEQ Level 7)

Teaching and Assessment

• Mode of study: In person
• Methods of assessment: 70% Exam, 30% Coursework
• Mark scheme: Numeric Marks

Other Information

• Number of students on module in previous year: 14
• Module leader: Dr Dmitry Adamskiy