Module 26428 (2013)
Syllabus page 2013/2014
06-26428
Machine Learning
Level 3/H
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus
The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)
Relevant Links
Outline
The module will provide a solid foundation to machine learning. It will give an overview of many of the core concepts, methods, and algorithms in machine learning, covering several forms of supervised and unsupervised learning. It also introduces the basics of computational learning theory, leading up to more advanced topics like boosting and ensemble methods. The module will give the student a good understanding of how, why and when do various modern machine learning methods work. It will also give them experience of applying machine learning methods in practice, and an awareness of the issues, techniques and open problems posed by high dimensionality in machine learning.
Aims
The aims of this module are to:
- introduce the basic concepts and terminology of machine learning
- give an overview of the main approaches to machine learning
- show similarities and differences between different approaches
- present basic principles for the classification of approaches to machine learning
- give practical experience of applying machine learning algorithms to classification and data analysis problems
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | demonstrate a knowledge and understanding of the main approaches to machine learning | Examination |
| 2 | demonstrate the ability to apply the main approaches to unseen examples | Examination, Continuous Assessment |
| 3 | demonstrate an understanding of the differences, advantages and problems of the main approaches in machine learning | Examination, Continuous Assessment |
| 4 | demonstrate an understanding of the main limitations of current approaches to machine learning, and be able to discuss possible extensions to overcome these limitations | Examination, Continuous Assessment |
| 5 | demonstrate a practical understanding of the use of machine learning algorithms | Examination, Continuous Assessment |
Restrictions, Prerequisites and Corequisites
Restrictions:
May not be taken by anyone who has taken or is taking 06-20236 (Machine Learning (Extended)) or 02640
Prerequisites:
06-23069 (Introduction to AI) (or equivalent)
06-21254 (Mathematical Techniques for Computer Science) (or equivalent)
Co-requisites:
None
Teaching
Teaching Methods:
2 hrs lectures, exercise and laboratory classes per week.
Contact Hours:
Assessment
- Sessional: 1.5 hr examination (80%), continuous assessment (20%).
- Supplementary (where allowed): By examination only.
Recommended Books
| Title | Author(s) | Publisher, Date |
| Machine Learning | T Mitchell | McGraw Hill, 1997 |
| Reinforcement Learning: an Introduction | R Sutton & A. Barto | MIT Press, 1998 |
| Support Vector Machines and Other Kernel-Based Learning Methods | Nello Cristianini & John Shawe-Taylor | 2000 |
| Pattern Classification | R.O. Duda, P.E. Hart & D.G.Stork | 2000 |
| A First Course in Machine Learning | S Rogers & M Girolami | 2012 |
| Artificial Intelligence, a Modern Approach | Russell S & Norvig P | 1995 |
Detailed Syllabus
- Overview of machine learning. Basic notions, literature
- Supervised learning
- Generative algorithms
- Discriminative algorithms
- Computational learning theory basics
- Boosting and ensemble methods
- Unsupervised learning
- Clustering
- Learning for structure discovery
- Reinforcement learning basics
- Topics in learning from high dimensional data and large scale learning
Last updated: 22 August 2013
Source file: /internal/modules/COMSCI/2013/xml/26428.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus