Artificial Intelligence Methods

Code

COMP2001

School

Computer Science

Level

2

Credits

20

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. 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. Students will also explore the implementation and application of some AI techniques. 

Target Students

Available to Level 2 students in the School of Computer Science. Not available to students taking COMP2011. This module is part of the Artificial Intelligence, Modelling and Optimisation theme in the School of Computer Science.

Assessment

• 50% Coursework 1: Programming assignment(s)/exercise(s) which will involve writing of one or multiple programs implementing AI methods applied to specific problems and associated in-class tests. Reassessment for the module is 100% examination.
• 50% Exam 1 (1-hour): Reassessment for the module is 100% examination.

Assessed by end of spring semester

Educational Aims

Learning Outcomes

Knowledge and Understanding:

• Ability to describe some advanced AI techniques and have a good understanding of those techniques.

Transferrable Skills:

• The ability to use functional techniques to solve problems.

Intellectual Skills:

• Understand and describe various AI techniques and where they might be applied.

Professional Skills:

• Enhanced skills to write software to implement an AI method, the ability to evaluate available AI tools and techniques and select those appropriate to a given situation.

Transferable Skills:

• Problem solving, ability to compare and contrast based on understanding and experience of AI programming tools and techniques.

Conveners

• Dr Warren Jackson