The University of Southampton

COMP6229 Machine Learning (MSc)

Module Overview

Machine Learning is about extracting useful information from large and complex datasets. The subject is a rich mixture of concepts from function analysis, statistical modelling and computational techniques. The module will cover the fundamental principles in the subject, where you will learn the theoretical basis of how learning algorithms are derived and when they are optimally applied, and gain some hands-on experience in laboratory-bases sessions. It will lead to more advanced material covered in later modules of Computational Biology, Computational Finance and Advanced Machine Learning, modules in which you will see useful applications of what you learn here. Note that this module has higher requirements than COMP3206 Machine Learning which will be assessed by a different set of coursework.

Aims and Objectives

Module Aims

To introduce the mathematical foundations for machine learning and a set of representative approaches to address data-driven problem solving in computer science and artificial intelligence.

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • Underlying mathematical principles from probability, linear algebra and optimisation
  • The relationship between machine learning and neurophysiology
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Systematically work with data to learn new patterns or concepts
  • Gain facility in working with algorithms to handle data sets in a scientific computing environment
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Characterise data in terms of explanatory models
  • Use data to reinforce one/few among many competing explanatory hypotheses
  • Gain a critical appreciation of the latest research issues


Historical Perspective - Biological motivations: the McCulloch and Pitts neuron, Hebbian learning. - Statistical motivations Theory - Generalisation: What is learning? - The power of machine learning methods: What is a learning algorithm? What can they do? Probability - Probability as representation of uncertainty in models and data - Bayes Theorem and its applications - Law of large numbers and the Gaussian distribution - Markov and graphical models Supervised Learning - Classification using Bayesian principles - Perceptron Learning - Support Vector Machines and Kernel methods - Neural networks/multi-layer perceptrons (MLP) - Features and discriminant analysis Linear Algebra - Using matrices to find solutions of linear equations - Properties of matrices and vector spaces - Eigenvalues, eigenvectors and singular value decomposition Data handling and unsupervised learning - Principal Components Analysis (PCA) - Blind source separation using Independent Components Analysis (ICA) - K-Means clustering - Spectral clustering - Manifold learning Regression and Model-fitting Techniques - Linear regression - Polynomial Fitting - Kernel Based Networks Optimisation - Convexity - 1-D minimisation - Gradient methods in higher dimensions - Constrained optimisation - Dynamic Programming Case Studies - Example applications: Speech, Vision, Natural Language, Bioinformatics.

Learning and Teaching

Supervised time in studio/workshop6
Preparation for scheduled sessions10
Completion of assessment task18
Wider reading or practice76
Follow-up work10
Total study time150

Resources & Reading list

Mackay, David J. C.,. Information Theory, Inference and Learning Algorithms. 

Murphy, Kevin (2012). Machine Learning: A Probabilistic Perspective. 

Barber, David (2012). Bayesian Reasoning and Machine Learning. 

Bishop, Christopher M., (2006). Pattern Recognition and Machine Learning. 

James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. 



MethodPercentage contribution
Coursework 20%
Examination  (2 hours) 80%


MethodPercentage contribution
Examination  (2 hours) 100%

Repeat Information

Repeat type: Internal & External

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