Summary:
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This half credit course provides an introduction to
various areas of artificial intelligence.
At the end of the course you should be able to
- Explain the aims, history, and application fields of
artificial intelligence
- Demonstrate an understanding of data modelling and
autonomous agents
- Apply and discuss the advantages and disadvantages of various
search algorithms
- Demonstrate knowledge of formal systems and propositional
logic
- Explain sources of uncertainty and principles of probability
- Use Bayes rules to determine prior probabilities from
posterior probabilities, and find the odds
of events
- Describe the aims and components of the rough set data
analysis and apply its tools.
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| Prequisite: |
COSC 2P03 (60% minimum) or permission of the instructor.
COSC 2P93 is recommended. |
| Time & place: |
Tuesday, Friday 15.30 - 17.00, venue EA102 |
| Assessment: |
Two assignments (20% each), two class tests (90 minutes, 30% each)
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| Required reading: |
Selected chapters from David Poole, Alan Mackworth Artificial Intelligence: Foundations of
Computational Agents
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Selected chapters from I. Düntsch, G.
Gediga "Foundations of building intelligent artifacts" (will be
distributed)
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| Recommended reading: |
D. Michie, D.J. Spiegelhalter, C.C. Taylor (eds), Machine Learning, Neural and Statistical
Classification
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Tom Mitchell, Machine Learning |
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Nils J. Nilsson, The
Quest for Artificial Intelligence |