Course Description:

This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. In addition, it covers applications of decision trees, neural nets, SVMs and other learning paradigms.

Lecture Notes:

This section contains a complete set of lecture notes for the course. The notes contain lecture slides and accompanying transcripts. The transcripts allow students to review lecture material in detail as they study for upcoming assignments and quizzes.

Chapter 1: Introduction (PDF - 2.4 MB)

Chapter 2: Search (PDF 1 of 3 - 2.6 MB) (PDF 2 of 3 - 1.9 MB) (PDF 3 of 3 - 2.4 MB)

Chapter 3: Constraint Satisfactory Problems (CSP) and Games (PDF 1 of 2 - 2.4 MB) (PDF 2 of 2)

Chapter 4: Learning Introduction (PDF - 2.7 MB)

Chapter 5: Machine Learning I (PDF - 1.8 MB)

Chapter 6: Machine Learning II (PDF - 1.7 MB) (These notes are labeled as "Section 10.")

Chapter 7: Machine Learning III (PDF - 2.1 MB)

Chapter 8: Machine Learning IV (PDF - 2.1 MB)

Chapter 9: Logic I (PDF 1 of 2 - 1.6 MB) (PDF 2 of 2 - 2.1 MB)

Chapter 10: Logic II (PDF 1 of 2 - 2.1 MB) (PDF 2 of 2 - 2.0 MB)

Chapter 11: Logic Programming (PDF - 1.4 MB)

Chapter 12: Language Understanding (PDF 1 of 2 - 2.3 MB) (PDF 2 of 2 - 1.0 MB)