Artificial Intelligence

Artificial Intelligence lecture note

· โปรแกรมสำหรับ Assignment 1

· ประกาศคะแนนสอบกลางภาค 2110477 (AI-II)

· ประกาศคะแนนสอบกลางภาค 2110654 (AI)

· 2110654 (AI) Final Examination:

Place: Eng. 4, 19-16, Time: 9.00-12.00 (24 September, 2009) !!!

The slides used in the course:

• 1st-3rd weeks
• slide no.1: Introduction, Problem-Problem Space-Search  slide no 1
• slide no.2: Tabu Search  slide no 2
• 4th week
• slide no.3: Predicate Calculus  slide no 3
• 5th week
• slide no.4: Introduction to Prolog  slide no 4   (SWI-Prolog's Web Site)
• 6th-7th weeks
• slide no.5: Natural Language Processing slide no 5
• 8th weeks
• Midterm Examination (scope—from slide no.1 to slide no.4)
• 9th week
• slide no.6: Genetic Algorithm  slide no 6
• 10th week
• slide no.7: Rote Learning, Learning by Analyzing Differences, Version Spaces slide no 7
  (A Prolog Implementation of Version Space)
• 11th week
• slide no.8: Decision-Tree Learning, Explanation-Based Learning slide no 8
  (A Decision-Tree Software: See 5’s Web Site, download CUMiner and .NET Framework) (A Prolog Implementation of EBL)
• 12th week
• slide no.9: Inductive Logic Programming slide no 9
• 13th week
• slide no.10: Artificial Neural Networks slide no 10 (NeuroSolutions’ Web Site)
• 14th week
• slide no.11: Bayesian Learning slide no 11
• 15th week
• Other topics: slide no.12: Support Vector Machines slide no 12 slide no 12
• 16th week
• Final examination (scope—from slide no.6 to slide no.12)
• 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
• If you have comments, suggestions, etc., give them to me at http://www.cp.eng.chula.ac.th/course/forum/?cat=2110477

Assignment1: data.dat

References:

• E.Rich & K.Knight, Artificial Intelligence, Second Edition, McGraw-Hill
• S. Russell & P.Norvig, Artificial Intelligence: A Modern Approach, Second Edition, Prentice Hall.

T.Mitchell, Machine Learning, McGraw-Hill