CS781 – Artificial Intelligence Concept

· ประกาศคะแนนสอบกลางภาค

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·Final Examination:

Place: Eng. 4, 19-01, Time: 10.00-12.00 (29 July, 2004) !!!

Final Examination:

Place: Eng. 4, 17-01, Time: 8.30-10.30 (25 September, 2003) !!!

The slides used in the course:

• 1st week
• slide no.1: Introduction, Problem-Problem Space-Search  slide no 1
• 2nd week
• slide no.2: Tabu Search  slide no 2
• 3rd week
• slide no.3: Predicate Calculus  slide no 3
• 4th week
• slide no.4: Introduction to Prolog  slide no 4   (SWI-Prolog's Web Site)
• 5th week
• slide no.5: Natural Language Processing slide no 5
• 6th week
• Midterm Examination (scope--slide no.1, no.4)
• slide no.5: Natural Language Processing (Con’t)
• 7th week
• slide no.6: Genetic Algorithm  slide no 6
• 8th week
• slide no.7: Rote Learning, Learning by Analyzing Differences, Version Spaces slide no 7 (A Prolog Implementation of Version Space)
• 9th week
• slide no.8: Decision-Tree Learning, Explanation-Based Learning slide no 8 (A Prolog Implementation of EBL, A Decision-Tree Software: See 5’s Web Site, download CUMiner)
• 10th week
• slide no.9: Artificial Neural Networks slide no 9 (NeuroSolutions’ Web Site)
• 11-12th weeks
• slide no.10:. Bayesian Learning slide no 10
• slide no.11:. Support Vector Machines slide no 11
• 11th week
• slide no.10: Inductive Logic Programming slide no 11
• 13th week
• Final Examination

Place: Eng. 3-215, Time: 8.30-10.30 (24 July,2003) !!!

The lecture notes:

• Lecture note no.1: Introduction to Artificial Intelligence  lecture note no 1
• Lecture note no.2: Problem, Problem Space and Search  lecture note no 2
• Lecture note no.3: Predicate Logic  lecture note no 3
• Lecture note no.4: Prolog  lecture note no 4
• Lecture note no.5: Genetic Algorithm  lecture note no 5
• Lecture note no.6: Rote Learning  lecture note no 6
• Lecture note no.7: Learning by Analyzing Differences  lecture note no 7
• Lecture note no.8: Version Space  lecture note no 8
• Lecture note no.9: Decision Tree Learning  lecture note no 9
• Lecture note no.10: Explanation-Based Learning  lecture note no 10
• Lecture note no.11: Neural Networks  lecture note no 11
• Lecture note no.12: Bayesian Learning  lecture note no 12
• Artificial Intelligence  lecture note

References:

• E.Rich & K.Knight, Artificial Intelligence, Second Edition, McGraw-Hill
• T.Mitchell, Machine Learning, McGraw-Hill