Artificial Intelligence Methods
| Code | School | Level | Credits | Semesters |
| COMP2051 | School of Computer Science | 2 | 20 | Spring China |
- Code
- COMP2051
- School
- School of Computer Science
- Level
- 2
- Credits
- 20
- Semesters
- Spring China
Summary
COMP1037 Fundamentals of Artificial Intelligence and COMP1039 Programming Paradigms are the prerequisites of this module
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 intelligent agents, and modern search techniques such as Genetic Algorithms, Tabu Search, Simulated Annealing, and Genetic Programming, etc.
Target Students
Compulsory for Part I Computer Science and Artificial Intelligence (EA07) students. Available to Part I Computer Science (EA05) students. Not available to students taking AE2AMI (10cr).
Classes
- One 2-hour lecture each week for 12 weeks
Assessment
- 50% Coursework
- 50% Exam 1 (2-hour): 2 hour exam. 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.