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Description
Aims:
The module introduces probabilistic modelling, covering the broad theoretical landscape, and aims to cover much of the first 12 chapters of the . The emphasis is on probabilistic modelling of discrete variables.
Intended 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.
Indicative content:
The following are indicative of the topics the module will typically cover:
- 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.
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听 formally available; (2) have understanding of and abilities with Linear Algebra, Multivariate Calculus and Probability at mathematics FHEQ Level 4 or above; and (3) have 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).
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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