The University of Southampton

COMP6208 Advanced Machine Learning

Module Overview

- To introduce key concepts in pattern recognition and machine learning; including specific algorithms for classification, regression, clustering and probabilistic modeling. - To give a broad view of the general issues arising in the application of algorithms to analysing data, common terms used, and common errors made if applied incorrectly. - To demonstrate a toolbox of techniques that can be immediately applied to real world problems, or used as a basis for future research into the topic.

Aims and Objectives

Module Aims

To provide an overview of advanced machine learning

Learning Outcomes

Knowledge and Understanding

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

  • Key concepts, tools and approaches for pattern recognition on complex data sets
  • Kernel methods for handling high dimensional and non-linear patterns
  • State-of-the-art algorithms such as Support Vector Machines and Bayesian networks
  • Theoretical concepts and the motivations behind different learning frameworks
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Be able to solve real-world machine learning tasks: from data to inference
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Conceptually understand the role of pattern analysis and probabilistic modeling, together with the mathematical techniques this requires


Key concepts - Supervised/Unsupervised Learning - Loss functions and generalization - Probability Theory - Parametric vs Non-parametric methods - Elements of Computational Learning Theory Kernel Methods for non-linear data - Support Vector Machines - Kernel Ridge Regression - Structure Kernels - Kernel PCA - Latent Semantic Analysis Bayesian methods for using prior knowledge and data - Bayesian inference - Bayesian Belief Networks and Graphical models - Probabilistic Latent Semantic Analysis - The Expectation-Maximisation (EM) algorithm - Gaussian Processes Ensemble Learning - Bagging - Boosting - Random Forest Dimensionality Reduction - CCA, LDA, ICA, NMF - Canonical Variates - Feature Selection vs Feature Extraction - Filter Methods - Sub-space approaches - Embedded methods Low-Rank approaches - Recommender Systems Application areas - Security - Business - Scientific

Learning and Teaching

Independent Study104
Total study time150

Resources & Reading list

John Shawe-Taylor and Nello Cristianini (2004). Kernel Methods for Pattern Analysis. 

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

Information Theory, Inference, and Learning Algorithms.

Reasoning and Machine Learning.



MethodPercentage contribution
Examination  (2 hours) 66.667%
Report 33.333%


MethodPercentage contribution
Examination  (2 hours) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisites: COMP3206 OR COMP6229

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