Electronic Mail: email@example.com
Place: ICS 174
Time: TuTh 11:00 to 12:20
Office: ICS 424E
Office Hours: Monday, Thursday, 1:00 to 2:00 pm.
- 36371 DIS 1: Tue, 2-2:50 in ICS 243
- 36372 DIS 2: Wed 10-10:50 in ICF 101
Learn the basic AI techniques, the problems for which they
are applicable and their limitations. Topics covered include
search and heuristic search algorithms,
Knowledge-representation (logic-based and probabilistic-based)
inference and learning algorithms.
Academic honesty is taken seriously. It is the responsibility of
each student to be familiar with UCI's current academic honesty
policies. Please take the time to read the current UCI Senate
Academic Honesty Policies.
There will be 4-6 quizzes, approximately every 1-2 weeks
on the material covered in class up to that time.
The quizzes will account for 15-20% of your overall grade.
The lowest scored quiz will be dropped.
There will be one midterm exam, closed books which will account for
25% of the grade.
There will be 1 project and/or homeworks which will
account for 15-20% of the grade.
There will be a final exam, closed books during the final week
which will account for 40% of the grade.
Read ics.171 for announcements, answers to homework etc.
Also, please post
questions about homework or anything else there.
If you don't understand something, others probably don't either
and will have the same question.
Some handouts will be distributed during the quarter by the
Distribution Center, others will be available to buy in the Engineering
Lecture 1. Introduction and overview: Goals, history,
intelligent agents. Ch. 1, 2.
Lecture 2. Problem solving: State-spaces, search graphs,
problem spaces, problem types. Ch. 3 .
Lecture 3-4. Uninformed search: greedy search, breadth-first,
depth-first, iterative deepening, bidirectional search. Ch. 3.
Lecture 5. Informed Heuristic search: Best-First, A*,
Properties of A*. Ch. 4.
Lecture 6. Informed Heuristic search: Branch and bound, IDA*,
Generating 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: forward-checking,
solving trees, iterative improvement (hill climbing, stochastic
local search.) Ch. 4.4.
Lecture 10. Representation and Reasoning: Propositional
logic, inference, resolution, satisfiability. Ch. 6.
Lecture 11. Predicate logic: Syntax, Quantifiers, variables.
Lecture 12. Inference in logic: Forward and backward
inference, unification. Ch. 9.
Lecture 13. Midterm:
Lecture 14. Inference in logic (continued)
Lecture 15. Applications to planning, Ch.11:
Lecture 16. Agent reasoning under uncertainty, Ch. 15
Lecture 17. Learning from observations: Learning decision
trees. Ch. 18.
Lecture 18. Perceptron model and neural networks, Ch. 19,
Lectures 19-20. Additional topics (reasoning with uncertainty,
natural language, vision)
as time permits
Resources on the Internet
A list of Web
resources about AI .