Advanced Machine Learning | COMP6208 | University of Southampton

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.

Linked modules

Pre-requisites: COMP3206 or COMP3223 or COMP6229 or COMP6245

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

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

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

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

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

Syllabus

Key concepts

Supervised/Unsupervised Learning
Loss functions and generalization
Probability Theory
Elements of Computational Learning Theory

Kernel Methods for non-linear data

Support Vector Machines
Kernels

Bayesian methods for using prior knowledge and data

Bayesian inference
Bayesian Belief Networks and Graphical models
Gaussian Processes

Ensemble Learning

Bagging
Boosting
Random Forest

Learning and Teaching

Study time
Type	Hours
Teaching	36
Independent Study	114
Total study time	150

Resources & Reading list

Internet Resources

Information Theory, Inference, and Learning Algorithms.

Reasoning and Machine Learning.

Textbooks

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

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

Aurelien Geron. Hands-On Machine Learning with Scikit-Learn & Tensor Flow.

Assessment

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Continuous Assessment	20%
Final Assessment	80%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Set Task	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Set Task	100%

Repeat Information

Repeat type: Internal & External