The learning strategy for the module requires students to commit 150 learning hours, of this there will be 38 hours of class support and 112 hours of independent and self directed study.

Lectures/ lab work/ Presentations /Tutorial
Student managed learning/ directed learning

Apart from the lecture each week, various other methods as mentioned above will be used as learning strategies.
There will be laboratory and tutorial sessions, students will be required to discuss and present various topics of the module in the class. Students will be required to apply theoretical knowledge in practical contexts.
Students are expected to be able to structure their own work and to work relatively independently under the guidance of the module teaching staff.

Topics will be drawn from

- Introduction to AI
- Introduction to the Lab exercises
- Introduction to Frames and Rules
- Knowledge Representation, Logic, and Language
- Problem solving and search
- Uncertainty, Probability, Bayes Rule, and Belief Nets
- Introduction to Planning
- Knowledge based systems, agents, time, space, and ontology Learning

Assignment (Learning outcomes 1,2,3and 4)

To pass this module student must obtain 40% marks. Re-assessment is capped at 40%

The VLE (NETED)
The Internet
Appropriate Software
Word Processing software for use in the coursework
Printed and electronic journals.
Computer system and other devices' manuals

S. Russell and P. Norvig. (2003) Artificial Intelligence A Modern Approach. Prentice­Hall,
R. O. Duda, P. E. Hart, and D. G. Stork. (2001) Pattern Classification. Wiley, 2nd edition,
T. Mitchell. (1997). Machine Learning. McGraw­Hill

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