Module 11352 (2001)
Syllabus page 2001/2002
06-11352
AI Techniques A
Level 1/C
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 AI Techniques A
Web-page for course material and further useful links.
Outline
The module provides a general introduction to artificial intelligence and its techniques. The focus of the module is on the presentation of knowledge representation techniques, knowledge acquisition and reasoning. An overview on the main subfields of artificial intelligence will be provided.
Aims
The aims of this module are to:
- provide a general introduction to artificial intelligence and its techniques
- give an overview on knowledge representation techniques as well as on the main subfields of artificial intelligence
- show the need for different knowledge representation formalisms
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | structure the field of artificial intelligence into its main subfields, and outline the important features of AI systems | Continuous assessment, examination |
| 2 | explain some of the most important knowledge representation formalisms and understand why there are different ones, discuss their advantages and drawbacks, and represent knowledge in unseen easy examples in any of them | Continuous assessment, examination |
| 3 | apply simple uninformed search algorithms | Continuous assessment, examination |
| 4 | understand the processes involved in building Expert Systems | Continuous assessment, examination |
| 5 | provide examples of different types of AI systems, and explain their differences, common techniques, and limitations | Continuous assessment, examination |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
None
Co-requisites:
06-11353 (AI Techniques B) (linked module)
Teaching
Teaching Methods:
3 hrs/week of lectures and exercise classes
Contact Hours:
Assessment
- Supplementary (where allowed): As the sessional assessment
- 3 hr examination (80%), continuous assessment (20%), divided equally between this module and 06-11353 (AI Techniques B). Resit by examination only.
Recommended Books
| Title | Author(s) | Publisher, Date |
| Artificial Intelligence: A New Synthesis | Nilsson N J | Morgan Kaufmann, 1998 |
| Artificial Intelligence; A Modern Approach | Russell S & Norvig P | Prentice Hall, 1995 |
| Artificial Intelligence (3rd edn) | Winston P H | Addison Wesley, 1992 |
| Artificial Intelligence (2nd edn) | Rich E & Knight K | McGraw Hill, 1991 |
| Expert Systems (3rd edn) | Jackson P | Addison Wesley, 1999 |
Detailed Syllabus
- The history and the dream of AI
- What is AI? - The roots, the goals, the subfields
- Cognitivism and the birth of AI
- Real Intelligence - Neural Network Systems
- Brain Modelling and Experimental Testing
- Information Processing and Learning in Artificial Systems
- Evolutionary Computation
- Knowledge Representation I - Foundations
- AI Application - Vision
- AI Application - Speech
- Knowledge Representation II - Frames
- AI Application - Robotics
- Knowledge Representation III - Semantic nets, inheritance
- Production Systems - Recognize-Act-Cycle, Matching, Conflict Resolution
- Uninformed Search
- Expert Systems I - Building a system
- Uncertainty treatment I - Probabilistic, Mycin
- Expert Systems II - Rule learning
- Uncertainty treatment II - Fuzzy Logic, Bayes Nets
- Limitations and Misconceptions of AI
- Philosophical Issues
Last updated: 27 September 2001
Source file: /internal/modules/COMSCI/2001/xml/11352.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus