Module 20236 (2008)
Syllabus page 2008/2009
06-20236
Machine Learning (Extended)
Level 4/M
Robert Durrant
10 credits in Semester 1
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
None
Outline
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 (with the exception of neural nets, which are dealt with in a separate module)
- 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 relationship between machine learning and human learning | Examination |
| 5 | 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 |
| 6 | Demonstrate a practical understanding of the use of machine learning algorithms | Continuous Assessment |
| 7 | survey and discuss the research literature in one subfield of machine learning | Continuous Assessment |
Restrictions, Prerequisites and Corequisites
Restrictions:
May not be taken by anyone who has taken or is taking 06-02640 (Machine Learning).
Prerequisites:
An introduction to AI, such as 06-18188 (Introduction to AI)
Co-requisites:
None
Teaching
Teaching Methods:
2 hrs lectures, exercise and laboratory classes per week.
Contact Hours:
Assessment
- Sessional: 1.5 hr examination (60%), continuous assessment (40%).
- 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 |
| Modelling the Internet and the Web | Pierre Baldi, Paolo Frasconi, and Padhraic Smyth | 2003 |
| Artificial Intelligence, a Modern Approach | Russell S & Norvig P | 1995 |
Detailed Syllabus
- Basic notions of learning, introduction to learning algorithms, literature
- Probabilistic Sequence models (Random sequence model, 1st order Markov model)
- Reinforcement Learning in deterministic environments
- Reinforcement Learning in non-deterministic environments
- Unsupervised Learning; Dimensionality reduction; Latent Semantic Analysis
- Independent Component Analysis. The cocktail party problem.
- Introduction to Bayesian learning; Naive-Bayes classification
- Decision Tree Learning
- Instance-Based Learning (k-nearest neighbour, Case-based Reasoning)
- Support Vector and Kernel Machines
- Clustering and visual data analysis
- Revision
Last updated: 7 Feb 2006
Source file: /internal/modules/COMSCI/2008/xml/20236.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus