Accessibility statement

# Machine Learning & Probabilistic Graphical Models - COM00032H

« Back to module search

• Department: Computer Science
• Module co-ordinator: Dr. James Cussens
• Credit value: 10 credits
• Credit level: H
• Academic year of delivery: 2019-20

## Module summary

This module presents a number of methods for machine learning (ML) where patterns are extracted from data to make predictions about future data and/or to understand the process that generated the data. It also presents "probabilistic graphical models" (PGM) which use graphs to represent probabilistic relations between variables (e.g. those representing observed data and those representing future data). The two topics are connected in the following way: many machine learning methods can be usefully represented using a PGM.

None

• None

• None

## Module will run

Occurrence Teaching cycle
A Spring Term 2019-20 to Summer Term 2019-20

## Module aims

This module aims to introduce a number of key machine learning methods and to connect them, where appropriate, to probabilistic graphical models. A key goal is to connect learning from data to reasoning with uncertainty using probability theory.

## Module learning outcomes

On completion of this module, students will:

1. Understand how probabilistic graphical models represent relations of conditional independence between variables
2. Understand the assumption behind using the following methods for data analysis: PCA, LDA, Kernel methods, SVMs, naive Bayes
3. Know when it is appropriate to use the following methods for data analysis: PCA, LDA, Kernel methods, SVMs, naive Bayes
4. Know how to model complex machine learning problems using the BUGS software
5. Understand the EM algorithm and its efficient implementation for a hidden Markov model

## Module content

• Bayesian networks
• Naive Bayes
• Markov networks
• Markov chain Monte Carlo (MCMC) using BUGS
• Hidden Markov models
• The Expectation-Maximisation (EM) algorithm
• Principal Components Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Kernels
• Support Vector Machines

## Assessment

Task Length % of module mark
Essay/coursework
Essay: Open Assessment
N/A 100

None

### Reassessment

Task Length % of module mark
Essay/coursework
Essay: Open Assessment
N/A 100

## Module feedback

1. Students will have a lightweight formative open assessment half way through the module for which they will get feedback.
2. Students will get feedback on their work in the summative open assessment.