1. module information
COM850C2/COM839J2
2003-4 sem 2
Intelligent Systems
Teaching Staff Contact Details
Name E-mail Phone Office
Ray J. Hickey rj.hickey@ulster.ac.uk 70 324603 D064
Michaela M. Black mm.black@ulster.ac.uk 70 323701 D074
Support Staff Contact Details
Ivan Houston ihouston@infc.ulst.ac.uk 70 324702 D051
Consultation Times
We are available throughout the semester at the times below for consultation in person, by
phone or through WebCT.
Mon Tues Weds Thurs Fri
13.15-14.15(MB) 10:15-12.15(MB) 11:15-13:15(MB)
14:15-16:15 (RH) 14:15-16:15 (RH) 14:15-16:15 (RH)
14.15-16.05(IH)
Aims
The module will provide students with an appreciation of the purposes, capabilities and range
of applications of systems that deploy Artificial Intelligence (AI) techniques, including
Machine Learning (ML). Students will gain experience in using an AI development
environment and a data mining package.
Learning Outcomes
(i) Identify the tasks that are likely to benefit from an AI approach
Propose structures for the representation of knowledge within a computer system and
(ii)
for reasoning mechanisms appropriate to these structures.
Assess the level of intelligence in currently deployed systems including expert
(iii)
systems and agents;
(iv) Suggest a range of current and potential uses for ML particularly within KDD.
Explain the basic principles of the symbolic and neural learning paradigms and
(v)
describe several ML/data mining algorithms.
Select, with justification, learning/mining algorithm(s) to solve particular knowledge
(vi)
acquisition tasks.
(vii) Develop programs in an AI language.
Timetable
The module is delivered by e-learning through WebCT. There will be an initial meeting on
Wednesday 28th January 2004 at 3:00 pm in Labs D072/D075 in South Building, Coleraine.
This should last about one hour.
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2. Teaching Schedule
The module is presented in a series of reading and practical study blocks (all available from
the module web-site). Students are advised to follow the schedule of work presented below.
Week 1 Initial Class
Week 2 What is Artificial Intelligence? (Reading)
Beginning Prolog (Practical)
Week 3 Logic for Knowledge Representation and Reasoning (Reading)
Rules and Beyond (Practical work)
Week 4 Fuzzy Logic (Reading)
Recursion and Lists (Practical work)
Week 5 Problem Solving in AI – Search Algorithms (Reading)
Problem Solving using Prolog (Practical work)
Week 6 School reading week
Week 7 AI Systems (Reading)
Fuzzy Logic in Prolog (Practical work)
Week 8 Introduction to Machine Learning and KDD (Reading)
Introduction to Clementine (Practical)
Week 9 Learning to Classify (Reading)
Data Pre-processing in Clementine (Practical work)
Week 10 Learning Decision Trees for Classification (Reading)
Building Decision Tree Models for Classification (Practical work)
Week 11 Artificial Neural Networks for Classification (Reading)
Building Neural Network Models for Classification
Week 12 Discovery (Reading)
Building Cluster Models (Practical work)
Directed Reading
Details of required and recommended reading and further material for independent study are
provided on the module website.
Coursework
There are three assignments worth, in total, 50% of the module marks:
Assignment Worth Hand-out Submission date Hand-back
1 Mini-project in 15% Weds 28 Jan 2004 Fri 5 March 2004 Fri 19 March 2004
Prolog (week 6)
2 Essay on 20% Weds 28 Jan 2004 Fri 26 March 2004 Fri 9 April 2004
research topic (week 9)
3 Mini-project in 15% Weds 28 Jan 2004 Fri 30 April 2004 Fri 14 May 2004
Clementine (week 12)
Full details of the assignments (including assessment criteria, marking scheme and
submission requirements) are available from the module website. In addition to provision in
labs, a disk containing the software for assignments 1 and 3 is available for home use.
Written Examination
The examination, worth 50% of module marks, will consist of a single two-hour paper
offering a choice of three questions from five. There are no compulsory questions.
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