The University of Southampton
Courses

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 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

TypeHours
Teaching36
Independent Study114
Total study time150

Resources & Reading list

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

Information Theory, Inference, and Learning Algorithms.

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

Reasoning and Machine Learning.

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

Assessment

Summative

MethodPercentage contribution
Examination  (2 hours) 60%
Report 40%

Referral

MethodPercentage contribution
Examination  (2 hours) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisites: COMP3206 or COMP3223 or COMP6229 or COMP6245

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