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

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 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 Introduction to Deep Learning

Learning and Teaching

Type	Hours
Independent Study	114
Teaching	36
Total study time	150

Resources & Reading list

Reasoning and Machine Learning.

Information Theory, Inference, and Learning Algorithms.

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

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

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

Assessment

Summative

Method	Percentage contribution
Examination (2 hours)	60%
Report	40%

Repeat

Method	Percentage contribution
Examination	100%

Referral

Method	Percentage contribution
Examination (2 hours)	100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisites: COMP3206 or COMP3223 or COMP6229 or COMP6245