(First Year Undergraduate)

Introduction to Artificial Intelligence - Course Material and Useful Links

Dr John A. Bullinaria


I no longer teach this module, but this web-page is now sufficiently widely used that I will leave it in place. It contains all the overheads, handouts, and exercise sheets used in the lectures, details about the examination, and so on, for the academic year 2005/6.

The aim of this module is to provide a general introduction to artificial intelligence, its techniques, and main subfields, suitable for students in their first term of all our core Computer Science programmes, as well as students beginning our AI half degree. There will be a main lecture and exercise class each week covering the main ideas, plus a parallel series of guest lectures covering a selection of more advanced and specialist applications.

The following table shows the module structure and lecture timetable. All the module handouts will be made available here as pdf files shortly after the paper versions have been distributed in the lectures. Spare paper copies will be deposited in the School library.

Colour coding: Black = Regular Lecture, Green = Exercise Session, Blue = Guest Seminar.

Week Session 1
Tuesdays 16:00-17:00
Session 2
Tuesdays 17:00-18:00
Session 3
Wednesdays 11:00-12:00
1 Module Organisation [pdf] -
2 The Roots, Goals and Sub-fields of AI [pdf] Exercise Session 1 [pdf] Evolutionary Computation [html]
(Thorsten Schnier)
3 Biological Intelligence and Neural Networks [pdf] Exercise Session 2 [pdf] Neural Network Applications [pdf]
(Peter Tino)
4 Building Intelligent Agents [pdf] Exercise Session 3 [pdf] Interacting Agent Based Systems [pdf]
(Dean Petters)
5 Knowledge Representation [pdf] Exercise Session 4 [pdf] AI and Philosophy [html]
(Aaron Sloman)
6 Semantic Networks and Frames[pdf] Exercise Session 5 [pdf] Natural Language Processing
(Mark Lee)
7 Production Systems [pdf] Exercise Session 6 [pdf] Intelligent Robotics
(Jeremy Wyatt)
8 Search [pdf] Exercise Session 7 [pdf] Computer Vision
(Ela Claridge)
9 Expert Systems [pdf] Exercise Session 8 [pdf] Computer Chess [pdf]
(Colin Frayn)
10 Treatment of Uncertainty [pdf] Exercise Session 9 [pdf] AI in Computer Games [pdf]
(Nick Hawes)
11 Machine Learning [pdf] Exercise Session 10 [pdf] Machine Learning Applications
(Ata Kaban)

12 Revision Lecture Covering the Whole Module [pdf]

Assessment will be by a 90 minute examination in May. For formal details about the aims, learning outcomes and assessment you should look at the official Module Description Page and Syllabus Page.

For students who manage to fail the examination at first attempt, there will be a resit examination in August. This will have the same format as the original examination.

For those of you interested in looking at some existing AI applications, the following links may be a good place to start:

The Recommended Books for this module are:

Title Author(s) Publisher, Date Comments
Artificial Intelligence: A Modern Approach S. Russell & P. Norvig Prentice Hall, 2003 This is the book that ties in most closely with the module
Artificial Intelligence (2nd edn) E. Rich & K. Knight McGraw Hill, 1991 Quite old now, but still a good second book
Artificial Intelligence: A New Synthesis Nils Nilsson Morgan Kaufmann, 1998 A good modern book
Introduction to Expert Systems (3rd edn) Peter Jackson Addison Wesley, 1999 The best book on Expert Systems
Artificial Intelligence (3rd edn) Patrick Winston Addison Wesley, 1992 A classic, but not advanced enough now
Artificial Intelligence Rob Callan Palgrave Macmillan, 2003 A good modern book
Artificial Intelligence Michael Negnevitsky Addison Wesley, 2002 A good modern approach
Artificial Intelligence (5th edn) George Luger Addison Wesley, 2004 Some students may prefer this one

If you can only afford to buy one book for this module, I would recommend getting the one by Russell & Norvig.

This page is maintained by John Bullinaria. Last updated on 3 October 2006.