THE UNIVERSITY
OF BIRMINGHAM		Computer Science

SYLLABUS PAGE, 2004/05

06-08775
Introduction to AI

Level 2/I

Dr M Kerber
10 credits in Sem1

Programmes | Modules | Updates | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus | Links

The School of Computer Science Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)

Changes and Updates

Most recent update: 30 Sep 2004.

Changes possible until the start of the academic year.

Outline

The module provides an introduction to artificial intelligence for the benefit of students not taking a degree or half-degree in artificial intelligence. It provides knowledge that is useful in itself for computing researchers and practitioners, as well as providing essential background for more specialised artificial intelligence modules in the School. The main topics addressed (in outline) include: the relevance of AI both to contemporary computing applications and to other academic disciplines; the relative merits of various broad computation styles in AI (eg symbolic and neural); theoretical underpinnings of AI; major practical AI techniques.

Aims

The aims of this module are to:

Learning Outcomes

On successful completion of this module, the student should be able to:Assessed by:
Explain some of the main knowledge representation formalisms and algorithms used in AI.Examination
Apply them to example problems.Project and/or Examination
Discuss their advantages and drawbacks.Project and/or Examination
Explain some of the relationships between the subfields of AI and their techniques.Examination
Demonstrate a knowledge of search algorithms and their properties.Examination
Make informed decisions about what AI techniques to consider in particular domains.Project and/or Examination

Restrictions, Prerequisites and Corequisites

Restrictions:

Not available to students on the Artificial Intelligence & Computer Science degree or the half-degree in Artificial Intelligence.

Prerequisites:

None

Co-requisites:

None

Teaching

Teaching methods:

2 hrs lectures per week, 1 hr lab/exercise class

Contact hours:

32

Assessment

2 hr examination (70%), continuous assessment (30%). Resit by examination only.

The continuous assessment consists of a team project.

Recommended Books

TitleAuthor(s)Publisher, Date
Artificial intelligence: A New SynthesisN NilssonMorgan Kaufmann, 1998
Artificial Intelligence: A Modern ApproachS Russell & P NorvigPrentice Hall, 1995

Detailed Syllabus

  1. Overview of the field: aims, methods, subfields, history, prospects.
  2. Knowledge Representation formalisms.
  3. Search: uninformed and informed techniques; properties.
  4. Logic: Predicate Calculus, English -> FOPC.
  5. Robotics: the Shakey project, behaviour based systems.
  6. Neural Networks: Overview, Delta Rule, learning as search.
  7. Computer Vision: Problems and low level operators.
  8. Machine Learning: Classification using Decision Trees.
  9. Evolutionary Approaches: the simple GA; GP; applications.

Relevant Links

See the Introduction to AI Web site.

Programmes | Modules | Updates | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus | Links

Page maintained by:Dr P Coxhead
Content last updated:30 Sep 2004
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