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.

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
Theoretical concepts and the motivations behind different learning frameworks
Key concepts, tools and approaches for pattern recognition on complex data sets
Kernel methods for handling high dimensional and non-linear patterns

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

Learning and Teaching

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

Resources & Reading list

Internet Resources

Reasoning and Machine Learning.

Information Theory, Inference, and Learning Algorithms.

Textbooks

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

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

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
Final Assessment	80%
Continuous Assessment	20%

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