Module 02640 (2011)
Syllabus page 2011/2012
The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)
The module will provide a good foundation to machine learning. It will compare and contrast human learning with machine learning. It will examine the limitations of machine learning, the role of hypothesis bias and hypothesis representation.
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
|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|
May not be taken by anyone who has taken or is taking 06-20236 (Machine Learning (Extended)).
06-23069 (Introduction to AI) (or equivalent)
2 hrs lectures, exercise and laboratory classes per week.
- Sessional: 1.5 hr examination (80%), continuous assessment (20%).
- Supplementary (where allowed): By examination only.
|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|
- 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
Last updated: 2 Mar 2006
Source file: /internal/modules/COMSCI/2011/xml/02640.xml