Module 02640 (2004)
Syllabus page 2004/2005
06-02640
Machine Learning
Level 2/I
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.)
Changes and updates
Assessment changed: 20% continuous assessment added.
Relevant Links
Outline
The module will provide a good foundation in 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.
Aims
The aims of this module are to:
- introduce the basic concepts and terminology of machine learning
- give an overview on the main approaches to machine learning (except neural nets, 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 |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
06-18188 (Introduction to AI) (or equivalent)
Co-requisites:
None
Teaching
Teaching Methods:
22 hrs lectures & exercise and lab classes
Contact Hours:
Assessment
- Supplementary (where allowed): As the sessional assessment
- 2 hr examination (80%), continuous assessment (20%). Resit by examination only.
Recommended Books
| Title | Author(s) | Publisher, Date |
| Machine Learning | T Mitchell | |
| Artificial Intelligence, a Modern Approach | Russell S & Norvig P | 1995 |
| Pattern Classification | R.O. Duda, P.E. Hart & D.G.Stork | 2000 |
| Reinforcement Learning: an Introduction | R Sutton & A. Barto | |
| Modelling the Internet and the Web | Pierre Baldi, Paolo Frasconi, and Padhraic Smyth | 2003 |
| Support Vector Machines and Other Kernel-Based Learning Methods | Nello Cristianini & John Shawe-Taylor | 2000 |
Detailed Syllabus
- Basic notions of learning, introduction to learning algorithms, literature
- Probabilistic Sequence models. Markov Models, Aggregate Markov Models, Basic notions of Hidden Markov Models
- Reinforcement Learning I (basic concepts, episodic and non-episodic tasks, reward function, Markov decision processes, policy, state value, Bellman's equations), Q-learning in deterministic environments
- Reinforcement Learning II (finding best policy, Dynamic Programming, Q learning in non-deterministic environments
- Unsupervised Learning. Dimensionality reduction. Latent Semantic Analysis
- Independent Component Analysis. The coctail party problem.
- Bayesian classification and clustering
- Visualisation and exploratory data analysis
- Decision Tree Learning
- Support Vector Machines and Kernel Machines
- Instance-Based Learning (k-nearest neighbour, Case-based Reasoning)
- Revision
Last updated: 21 Jan 2005
Source file: /internal/modules/COMSCI/2004/xml/02640.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus