Artificial Intelligence

Artificial Intelligence lecture note


· ประกาศคะแนนสอบกลางภาค 2110477 (AI-II)
· ประกาศคะแนนสอบกลางภาค 2110654 (AI)

· 2110477 (AI 2) Final Examination:
Place: Eng. 4, 19-01, Time: 9.00-12.00 (28 September, 2007) !!!
scope—from slide no.1 to slide no.4

· 2110654 (AI) Final Examination:
Place: Eng. 4, 19-01, Time: 9.00-12.00 (27 September, 2007) !!!
scope—from slide no.1 to slide no.4


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
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.