CS 2710  Foundations of Artificial Intelligence


Time:  MW 11:00-12:20pm,  Room 5313 (Sennott Square)

Instructor:  Milos Hauskrecht
5329 Sennott Square, x4-8845
e-mail: milos_at_cs.pitt.edu
office hours: Tuesday 3:00-4:00pm, Wednesday 1:00-2:00pm
TA: Tomas Singliar
5406 Sennott Square,
e-mail: tomas@cs.pitt,edu
office hours: Tuesday 10:00-11:30am, Thursday: 3:00-4:30pm



Announcements (check often)



Lectures  
 
Lectures  Topic(s)  Assignments
August 29 Administrivia and course overview.

Readings: RN - chapters 1, 2.

 
August 31 Problem solving by searching

Readings: RN - chapter 3, sections 3.1.-3.3.

 
September 7 Uninformed search methods

Readings: RN - chapter 3, sections 3.4.-3.5.

HW 1 assignment
Programs
HW1 Solution
Solution programs
September 12 Uninformed search methods (finish). Informed search.

Readings: RN - chapter 3, sections 3.4.-3.5, and chapter 4.

 
September 14 Informed search

Readings: RN - chapter 4

HW 2 Assignment
Programs
HW2 Solution
Solution programs
September 19 Constraint satisfaction search

Readings: RN - chapter 4

 
September 21 Search for the optimal configuration

Readings: RN - chapter 4.3, 4.4

HW 3 Assignment
Programs
HW3 Solution
Solution programs
September 26 Adversarial search

Readings: RN - chapter 6

 
September 26 Propositional logic

Readings: RN - chapter 7.1.-7.5.

HW 4 Assignment
Programs
HW4 Solution
Solution programs
October 3 Propositional logic

Readings: RN - chapter 7.

 
October 5 First order logic

Readings: RN - chapter 8

HW 5 Assignment
Programs
HW5 Solution
Solution programs
October 10 Inference in FOL

Readings: RN - chapter 8.

 
October 12 Logic reasoning systems. Situation calculus.

Readings: RN - chapters 9, 10

HW 6 Assignment
HW6 Solution
October 17 Planning.

Readings: RN - chapter 11.1-11.3.

 
October 19 Modeling uncertainty

Readings: RN - chapter 13.

 
October 24 Midterm exam

Readings: Search, Knowledge representation, Planning

 
October 26 Bayesian belief networks

Readings: RN - chapter 14.

HW 7 Assignment
HW 7 Solution
October 31 Inference in Bayesian belief networks

Readings: RN - chapter 14.

 
November 2 Inference in Bayesian belief networks

Readings: RN - chapter 14.

HW 8 Assignment
HW 8 Programs
HW8 Solution
Solution programs
November 7 Decision making in the presence of uncertainty I

Readings: RN - chapter 16.

 
November 9 Decision making in the presence of uncertainty II

Readings: RN - chapter 16.

HW 9 Assignment
HW 9 Solution
November 14 Learning

Readings: RN - Chapter 18.1-18.3

 
November 16 Linear and logistic regression

Readings: Chapter 20.5.

HW 10 Assignment
Programs
HW10 Solution
Solution programs
November 21 Multi-layer perceptron

Readings: Chapter 20.5.

 
November 28 No class  
November 30 Learning BBNs HW 11 Assignment
December 5 Reinforcement learning
December 7 Reinforcement learning (finish), Applied AI
December 12 Course review
December 14 Final Exam



Additional readings

A collection of links to related material can be found here.



Course description

This course will provide an introduction to the fundamental concepts and techniques underlying the construction of intelligent computer systems. Topics covered in the course include: problem solving and search, logic and knowledge representation, planning, reasoning and decision-making in the presence of uncertainty, and machine learning.

Prerequisites:   undergraduate level AI (CS 1571 or equivalent) or the permission of the instructor
 

Textbook:

Stuart Russell, Peter Norvig. Artificial Intelligence.  A modern approach. 2ed. Prentice Hall, 2002.
Note: The second edition of the book was published at the end of 2002. There are significant changes as compared to the first (1995) edition of the book. Please make sure to obtain the new (green color cover) edition.
 



Grading



Homeworks

There will be weekly homework assignments. The homeworks will include a mix of paper and pencil problems, and programming assignments. The assignments are due at the beginning of the class on the day specified on the assignment. In general, no extensions will be granted.

Programming assignments. Knowledge of C/C++ language is neccessary for the programming part. C/C++ programs submitted by you should compile with g++ compiler under unix. Please see the rules for submitting programming assignments.



Academic Honesty

All the work in this course should be done independently. Collaborations on homeworks are not permitted. Cheating and any other antiintellectual behavior, including giving your work to someone else, will be dealt with severely. If you feel you may have violated the rules speak to us as soon as possible.

Please make sure you read, understand and abide by the Academic Integrity Code for the Faculty and College of Arts and Sciences.


Students With Disabilities

If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services, 216 William Pitt Union, (412) 648-7890/(412) 383-7355 (TTY), as early as possible in the term. DRS will verify your disability and determine reasonable accomodations for this course.