Course
| code | CC753 |
| credit_hours | 3 |
| title | Advanced Topics in Artificial Intelligence |
| arbic title | |
| prequisites | |
| credit hours | 3 |
| Description/Outcomes | This course allows the introduction of material relating to current artificial intelligence research topics, and current advances in artificial intelligence technology. |
| arabic Description/Outcomes | |
| objectives | To write-up survey papers about a narrow topic, and implement software tools to practice the different advanced topics. |
| arabic objectives | |
| ref. books | - Russel, S., Peter Norvig, “Artificial Intelligence: A Modern Approachâ€, second edition, 2002.
- Elaine Riche, K.K, “Artificial Intelligenceâ€, McGraw Hill, 1983.
- Computational Intelligence and Modern Heuristics by Al-Dahoud Ali - InTech , 2010
- Encyclopedia of Computational Intelligence by Eugene M. Izhikevich, at al. - Scholarpedia , 2009
- Global Optimization Algorithms: Theory and Application. Thomas Weise. 2009, 2nd Edition
- Essentials of Metaheuristics by Sean Luke
- Introduction to Machine Learning by Nils J. Nilsson , http://ai.stanford.edu/~nilsson
- An Introduction to Genetic Algorithms by Melanie Mitchell
- Artificial Intelligence, 4th edition by G. Luger
|
| arabic ref. books | |
| textbook | |
| arabic textbook | |
| objective set | |
| content set | |
Course Content
| content serial |
Description |
| 1 |
Introduction
|
| 2 |
Heuristic Search
|
| 3 |
Combinatorial optimization
|
| 4 |
Introduction to NP-complete problems
|
| 5 |
Data mining techniques
|
| 6 |
Knowledge representation
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| 7 |
Bayesian models
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| 8 |
Decision trees, Classification rules, instance-base learning, Bayesian networks, and Markov chains
|
| 9 |
Bayesian classifiers
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| 10 |
Classifier Models
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| 11 |
CSP: Constrained Satisfaction Problems
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| 12 |
Genetic algorithms & Evolutionary computation
|
| 13 |
Machine Learning & Learning techniques
|
| 14 |
Fuzzy logic & Fuzzy-based systems
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| 15 |
Clustering and Data mining
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| 16 |
Reasoning under Uncertainty
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