Module 02640 (2001)
Syllabus page 2001/2002
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.)
Relevant Links
Outline
The course 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 course)
- show similarities and differences between different approaches
- present basic principles for the classification of approaches to machine learning
- To get practical(in-lab) experience of applying machine learning algorithms to unseen data
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 |
| 3 | demonstrate an understanding of the differences, advantages and problems of the main approaches in machine learning | Examination |
| 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 |
| 6 | demonstrate a practical understanding of the use of machine learning algorithms | Examination |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
First year AI courses
Co-requisites:
None
Teaching
Teaching Methods:
22 hrs lectures & exercise and lab classes
Contact Hours:
Assessment
- Supplementary (where allowed): As the sessional assessment
- NOTE: the assessment was changed in November 2001 to remove coursework.
Recommended Books
| Title | Author(s) | Publisher, Date |
| Reinforcement Learning: an Introduction | R Sutton & A. Barto | |
| Machine Learning | T Mitchell | |
| Artificial Intelligence, a Modern Approach | Russell S & Norvig P | 1995 |
| Artificial Intelligence (3rd edn) | Winston P H | 1992 |
| Artificial Intelligence (2nd edn) | Rich E & Knight K | 1991 |
Detailed Syllabus
- Basic notions of learning, introduction to learning algorithms, incremental learning, literature, Winston's concept learning program
- Decision Tree Learning
- Version Space Learning, Computational Learning Theory
- Reinforcement Learning I(basic concepts, episodic and non-episodic tasks, reward function, Markov processes, policy, state value, Bellman's equations)
- Reinforcement Learning II (finding best policy, Dynamic Programming, Monte Carlo methods, Temporal Difference Learning, Sarsa, Q learning
- explanation-based learning, analogy
- Instance-based learning (k-nearest neighbour, Case-based Reasoning
- Inductive Logic Programming (Inverse resolution, Predicate discovery)
- Genetic Algorithms, Genetic Programming
- Evolutionary Classifier Systems
- Case Studies and Applications
- Revision
Last updated: 16 November 2001
Source file: /internal/modules/COMSCI/2001/xml/02640.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus