School of Computer Science

Module 06-23069 (2011)

Introduction to AI

Level 1/C

Nicholas Hawes Semester 1 10 credits
Co-ordinator: Nicholas Hawes
Reviewer: Mark Lee

The Module Description is a strict subset of this Syllabus Page.

Aims

The aims of this module are to:

  • provide a general introduction to artificial intelligence, its techniques and its main subfields
  • give an overview of key underlying ideas, such as knowledge representation, reasoning, search, and learning
  • demonstrate the need for different approaches for different problems
  • provide a foundation for further study of specific areas of artificial intelligence

Learning Outcomes

On successful completion of this module, the student should be able to:

  • discuss the major issues and techniques in a variety of sub-fields of AI, such as vision, robotics, natural language processing, planning, probabilistic reasoning, and machine learning
  • compare common AI techniques, describing their strengths and limitations
  • apply a variety of standard AI techniques to simple examples

Teaching methods

2 hrs/week of lectures plus 16 hours of lab sessions over the semester


Assessment

  • Sessional: Coursework (20%), 1.5 hr examination (80%).
  • Supplementary: 1.5 hr examination only (100%)

Detailed Syllabus

  1. Introduction and background
  2. Decision Tree learning
  3. Neural Networks
  4. Probabilistic AI and Bayes inference
  5. Uninformed and informed search
  6. Planning
  7. Evolutionary Computation

Programmes containing this module