Module 08775 (2003)
Syllabus page 2003/2004
06-08775
Introduction to AI
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
See the Introduction to AI Web site.
Outline
The module provides an introduction to artificial intelligence for the benefit of students not taking a degree or half-degree in artificial intelligence. It provides knowledge that is useful in itself for computing researchers and practitioners, as well as providing essential background for more specialised artificial intelligence modules in the School. The main topics addressed (in outline) include: the relevance of AI both to contemporary computing applications and to other academic disciplines; the relative merits of various broad computation styles in AI (eg symbolic and neural); theoretical underpinnings of AI; major practical AI techniques.
Aims
The aims of this module are to:
- provide a general introduction to artificial intelligence (AI)
- give an overview of knowledge representation and the main sub-fields of AI
- show the need for different knowledge representation formalisms
- develop an understanding of important AI techniques and their applications
- provide a foundation for further study of specific areas of AI
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | explain some of the main knowledge representation formalisms and algorithms used in AI | Examination |
| 2 | apply them to example problems | Project and/or Examination |
| 3 | discuss their advantages and drawbacks | Project and/or Examination |
| 4 | explain some of the relationships between the subfields of AI and their techniques | Examination |
| 5 | demonstrate a knowledge of search algorithms and their properties | Examination |
| 6 | make informed decisions about what AI techniques to consider in particular domains | Project and/or Examination |
Restrictions, Prerequisites and Corequisites
Restrictions:
Not available to students on the Artificial Intelligence & Computer Science degree or the half-degree in Artificial Intelligence.
Prerequisites:
None
Co-requisites:
None
Teaching
Teaching Methods:
2 hrs lectures per week, 1 hr lab/exercise class
Contact Hours:
Assessment
- Supplementary (where allowed): As the sessional assessment
- The continuous assessment consists of a team project.
Recommended Books
| Title | Author(s) | Publisher, Date |
| Artificial intelligence: A New Synthesis | N Nilsson | Morgan Kaufmann, 1998 |
| Artificial Intelligence: A Modern Approach | S Russell & P Norvig | Prentice Hall, 1995 |
Detailed Syllabus
- Overview of the field: aims, methods, subfields, history, prospects.
- Knowledge Representation formalisms.
- Search: uninformed and informed techniques; properties.
- Logic: Predicate Calculus, English -> FOPC.
- Robotics: the Shakey project, behaviour based systems.
- Neural Networks: Overview, Delta Rule, learning as search.
- Computer Vision: Problems and low level operators.
- Machine Learning: Classification using Decision Trees.
- Evolutionary Approaches: the simple GA; GP; applications.
Last updated: 2 July 2003
Source file: /internal/modules/COMSCI/2003/xml/08775.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus