School of Computer Science

Module 06-30255 (2022)

Machine Learning and Intelligent Data Analysis (Extended)

Level 4/M

Iain Styles Kashif Rajpoot Leandro Minku Semester 1 20 credits
Co-ordinator: Iain Styles
Reviewer: Leandro Minku

The Module Description is a strict subset of this Syllabus Page.


Machine learning studies how computers can autonomously learn from available data, without being explicitly programmed. The 'information revolution' has generated large amounts of data, but valuable information is often hidden and hence unusable. The module will provide a solid foundation to machine learning and advanced data analysis. It will give an overview of the core concepts, methods, and algorithms for analysing and learning from data. The emphasis will be on the underlying theoretical foundations, illustrated through a set of methods widely used in practice. This will provide the student with a good understanding of how, why and when do various modern machine learning and data analysis methods work.

Learning Outcomes

On successful completion of this module, the student should be able to:

  • Demonstrate knowledge and understanding of core ideas and foundations of unsupervised and supervised learning on vectorial data
  • Explain principles and techniques for mining textual data
  • Demonstrate understanding of the principles of efficient web-mining algorithms
  • Demonstrate understanding of broader issues of learning and generalisation in machine learning and data analysis systems
  • The student should demonstrate the capacity to independently study, understand, and critically evaluate advanced materials or research articles in the subject areas covered by this module.

Taught with

  • 06-30229 - Machine Learning and Intelligent Data Analysis

Cannot be taken with

  • 06-30229 - Machine Learning and Intelligent Data Analysis


  • Main Assessments: 1.5 hour examination (80%) and continuous assessment (20%)
  • Supplementary Assessments: 1.5 hour examination (100%)

Programmes containing this module