Module 23069 (2009)
Syllabus page 2009/2010
06-23069
Introduction to AI
Level 1/C
Jeremy Wyatt:5
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
See the
Module Web Page [2009]
for module material and further useful links.
Outline
This module provides a general introduction to artificial intelligence, its techniques, and main subfields. The principal focus of the module will be on the common underlying ideas, such as knowledge representation, rule based systems, search, and learning. It will provide a foundation for further study of specific areas of artificial intelligence.
Aims
The aims of this module are to:
- provide a general introduction to artificial intelligence, its techniques and its main subfields
- give an overview of key underlying ideas, such as knowledge representation, reasoning, search, and learning
- demonstrate the need for different approaches for different problems
- provide a foundation for further study of specific areas of artificial intelligence
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | describe the major issues and techniques in a variety of sub-fields, such asvision, robotics, natural language processing, planning, probabilistic AI,and learning | Examination |
| 2 | provide examples of different types of AI systems, and explain their differences, common techniques, and limitations | Examination |
| 3 | explain some of the most important knowledge representation formalisms and why they are needed, discussing their advantages and disadvantages | Examination |
| 4 | apply a variety of standard AI algorithms and representations to simple examples | Examination, Continuous assessment |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
None
Co-requisites:
None
Teaching
Teaching Methods:
3 hrs/week of lectures, guest seminars, and exercise sessions
Contact Hours:
Assessment
- Sessional: Continuous Assessment (20%), 1.5 hr examination (80%).
- Supplementary (where allowed): 1.5 hr examination only (100%)
Recommended Books
| Title | Author(s) | Publisher, Date |
| Artificial Intelligence | Rob Callan | Palgrave Macmillan, 2003 |
| Artificial Intelligence: A Modern Approach (2nd edn) | S Russell & P Norvig | Prentice Hall, 2003 |
| Artificial Intelligence: A New Synthesis | N J Nilsson | Morgan Kaufmann, 1998 |
| Artificial Intelligence (5th edn) | G Luger | Addison Wesley, 2005 |
Detailed Syllabus
- An Introduction and background
- Biological Intelligence and Neural Networks
- Decision Tree learning
- Case study: machine learning of classifications for medicine
- Probabilistic AI and Bayes inference
- Uninformed and informed search
- Planning
- Natural Language Processing
- Constraint Satisfaction
- Intelligent Robotics
- Vision
- Evolutionary Computation
- Case study: AI in space
- Knowledge representation
Last updated: 17 Feb 2010
Source file: /internal/modules/COMSCI/2009/xml/23069.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus