• Professor: Rina Dechter
• Electronic Mail: dechter@ics.uci.edu
• Place: RH 101
• Time: TuTh 02:00 to 03:20p
• Office: ICS 424E
• Office Hours: Monday, Thursday, 1:00 to 2:00 pm.
• Textbooks:
• Artificial Intelligence: A Modern Approach, by Russell and Norvig.
• Classnotes
• Teaching Assistants
• Lucas Scharenbroich lscharen@uci.edu . Office hours, uesday,Thursday 10-11, CSE 301.
• Andrew Felch afelch@uci.edu Office hours are 3:30-4:30 Tuesday (after class) and 5:00-6:00 Thursday (after discussion), ETC 105
• Discussion Sections
• 36471 DIS 1: MO, 10:00-10:50 in ICF 101
• 36472 DIS 2: Wed 02:00-02:50 in ICF 101
• 36473 DIS 3: Thur 4:00-04:50 in ICF 101

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 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 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.

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 Copy Center.

• 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 heauristics, 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. Veteran day
• Lecture 15. Learning from observations. Chapter 18.
• 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

• A list of Web resources about AI .
• Computing Machinery and Intelligence, A.M. Turing