171 Course Outline, Fall 2006
- Professor: Rina
Dechter
- Email: dechter@ics.uci.edu
- Place: CS 174
- Time: Tuesday and Thursday 2:00 to
03:20 pm
- Office: ICS 424E
- Office Hours: Monday 2-3 pm and
Thursday 1-2 pm
- Textbooks:
- Artificial Intelligence: A Modern
Approach, by Russell and Norvig
(second edition).
- Classnotes
- Teaching Assistants
- Vadim Bichutskiy Email: vbichuts@uci.edu
Office Hours: Tuesday 10-11am and Friday 10-11am in CST 127A
- Reader
- Discussion Sections
(Note that there will be no discussion on 09/25/2006)
- Monday 12:00-12:50pm Location: HIB 110
- Monday 9:00- 9:50 am Location: HIB 110
Course Goals:
Learn the basic AI
techniques, the
problems for which they are applicable and their limitations. Topics
covered
include heuristic search algorithms, Knowledge-representation
(logic-based and
probabilistic-based) inference and learning algorithms.
Academic Honesty:
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.
Assignments:
- There will be weekly homeworks, about 7-8 throughout the quarter,
each on the material covered in class up to that time. Homeworks will account for 25-30% of the grade. Homeworks will be assigned on Tuesday and will
be due to following Thursday at 2:00 pm in class (stay tuned for
changes towards the end of the quarter) The
lowest scored homework will be dropped. There will be no make-ups for homeworks.
- There will be 1 project which will
account for 10-15% of the grade.
- There will be one midterm exam, closed
books which will account for 20% of the grade.
- There will be a final exam, closed
books during the final week which will account for 40% of the grade.
Bulletin Board:
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.
Procedures:
Some handouts will be
distributed
during the quarter by the Distribution
Center, others will be available to buy in the Engineering Copy Center.
Schedule:
Note that the
schedule is only tentative and will be
subject to change during the quarter
- Lecture 1. Introduction, history,
intelligent agents. Chapters 1, 2.
- Lecture 2. Problem formulation:
State-spaces, search graphs, problem spaces, problem types. Chapter 3 .
- Lectures 3. Uninformed search:
breadth-first, depth-first, iterative deepening, bidirectional search.
Chapter 3.
- Lectures 4. Informed Heuristic search:
Greedy, Best-First, A*, Properties of A*. Chapter 4.
- Lectures 5. Informed Heuristic search,
Properties and generating heuristics, constraint satisfaction. Chapters
4,5.
- Lecture 6. Constraint satisfaction.
Chapter 5.
- Lecture 7. Constraint satisfaction.
Game playing Chapter 6.
- Lecture 8. Game playing. Chapter 6.
- Lecture 9. Representation and
Reasoning: Propositional logic. Chapter 7.
- Lecture 10. Representation and
Reasoning: Inference in propositional logic. Chapter 7.
- Lecture 11. Midterm
- Lecture 12. First order logic.
Chapter. 9.
- Lecture 13. Inference in first order
logic. Chapter 9.
- Lecture 14. Learning from
observations. Chapter 18
- Lecture 15. Learning from
observations. Chapter 18 continued
- Lecture 16. Neural networks. Chapter,
20.5
- Lecture 17. Handling uncertainty.
Chapter 13
- Lecture 18. Thanksgiving
- Lecture 19.
Bayesian networks. Chapter 14
- Lecture 20.
Assorted topics
- Lecture 21:
Assorted topics
Resources on the Internet