Artificial Intelligence Methods (10cr)
| Code | School | Level | Credits | Semesters |
| COMP2011 | Computer Science | 2 | 10 | Spring UK |
- Code
- COMP2011
- School
- Computer Science
- Level
- 2
- Credits
- 10
- Semesters
- Spring UK
Summary
This module builds on the first year Fundamentals of 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 in AI, including fuzzy logic and modern search techniques such as Iterated Local Search, Tabu Search, Simulated Annealing, Evolutionary Algorithms, Genetic Algorithms and Hyper-heuristics.
Target Students
Available to Level 2 students in the School of Computer Science. Not available to students taking COMP2001. This module is part of the Artificial Intelligence, Modelling and Optimisation theme in the School of Computer Science.
Assessment
- 100% Exam 1 (1-hour): ExamSys exam.
Assessed by end of spring semester
Educational Aims
To build on the 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.