Artificial Intelligence Methods
| Code | School | Level | Credits | Semesters |
| COMP2039 | Computer Science | 2 | 10 | Spring Malaysia |
- Code
- COMP2039
- School
- Computer Science
- Level
- 2
- Credits
- 10
- Semesters
- Spring Malaysia
Summary
This module builds on the first year Introduction to AI, which covers the ACM learning outcomes, and introduces new areas. The emphasis is on building on the AI research strengths in the School. As a Launchpad it gives brief introductions to topics including AI techniques, fuzzy logic and planning, and modern search techniques such as, Iterated Local Search, Tabu Search, Simulated Annealing, Evolutionary Algorithms, Genetic Programming and Hyper-heuristics, etc.
Target Students
Available to Part I undergraduate students in the School of Computer Science who are not registered on G4G1 and G4G7. This module is part of the Artificial Intelligence and Modelling and Optimisation Theme in the School of Computer Science.
Classes
- One 2-hour lecture each week for 12 weeks
Activities may take place every teaching week of the Semester or only in specified weeks. It is usually specified above if an activity only takes place in some weeks of a Semester
Assessment
- 100% Exam 1 (2-hour): 120 minutes written examination. Answering questions on the topics taught in lectures.
Educational Aims
To build on first year AI module and further an appreciation of various AI techniques.Learning Outcomes
Knowledge and Understanding:
Ability to describe some advanced AI techniques and have a good understanding of AI techniques.
Intellectual Skills:
Understand and describe various AI techniques and where they might be applied.
Professional Skills:
The ability to understand available AI techniques and select those appropriate to a given situation.
Transferable Skills:
Problem solving, ability to compare and contrast AI techniques.
Conveners
- Dr Chye Cheah Tan
- Dr Tissa Chandesa