ICS 171: Introduction to Artificial Intelligence
Electronic Mail: firstname.lastname@example.org
Place: ICS 174
Time: TuTh 12:30 to 1:50
Office: ICS 424E
Office Hours : Mondays, 1:30 to 3:30 pm.
- 36371 DIS 1: Monday, 1:00 to 1:50, ICF 101
- 36372 DIS 2: Wednesday, 1:00 to 1:50, ICF 101
- 36373 DIS 3: Friday, 1:00 to 1:50, ICF 101
Topics Covered Include:
- Search for problem solving,
- Knowledge-representation and reasoning, logic approach,
- Knowledge-representation and reasoning under uncertainty,
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Bi-weakly homework assignment distributed in class each week:
the homework will be given on Tuesdays and should be turned in the
15-20 min. quiz every Thursday (almost).
One programming project.
Some handouts will be distributed during the quarter by the
Distribution Center, others will be available to buy in the Engineering
Homeworks plus project will account for 40%, quizzes - for 20% and
final - 40% of the course grade.
Lecture 1. Introduction and overview: Goals, history,
intelligent agents. Ch. 1, 2.
Lecture 2. Problem solving: Examples (n-queen, 8-puzzle, The
road map problem, traveling salesman), State-spaces, search graphs,
AND-OR graphs, problem spaces, problem types. Ch. 3 .
Lecture 3. Uninformed search: greedy search, breadth-first,
depth-first, iterative deepening, bidirectional search. Ch. 3.
Lecture 4. Uninformed search: continued. quiz 1. Ch. 3.
Lecture 5. Informed Heuristic search: Best-First, A*,
Properties of A*. Branch and bound. Ch. 4.
Lecture 6. Informed Heuristic search: Branch and bound, IDA*,
Inventing heuristics automatically. Ch. 4.
Lecture 7. Game playing: Minimax search, Alpha-Beta pruning.
Lecture 8. Constraint networks: The Constraint Satisfaction
problem formulation, constraint-graphs, Consistency algorithms.
Lecture 9. Search in CSPs: Backtracking, forward-checking,
processing special classes (trees). Class-notes.
>Lecture 10. Iterative improvement: Hill climbing, stochastic
search. Ch. 4.4.
Lecture 11. Representation and Reasoning: Propositional
logic, inference, resolution, satisfiability. Ch. 6.
Lecture 12. Predicate logic: Syntax, Quantifiers, variables.
Lecture 13. Inference in logic: Forward and backward
inference, unification. Ch. 9.
Lecture 14. Inference- continued: Ch. 9.
Lecture 15. Review: application to planning:The block world,
full vs partial order planning, STRIP.
Lecture 16 Representation and reasoning under uncertainty:
Lecture 17. Belief networks: Ch. 15.
Lecture 18. Learning from observations: Learning decision
trees. Ch. 18.
Lecture 19. Learning in Neural Networks: The perceptron. Ch.
Lecture 20. Summary and Overview
Resources on the Internet
A list of Web
resources about AI .