CS2710  Foundations of Artificial Intelligence


Time:  MW 2:30pm-3:45pm,  Langley Hall A 214 (LANGY A214)



Instructor:  Milos Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos at pitt edu
office hours: Tuesday 1:00-2:30pm, Wednesday 9:30am-10:30am


TA:  Patrick Luo
Computer Science Department
5406 Sennot Square
phone: 4-9182
e-mail: zhl78 at pitt edu
office hours: Monday 9:00am-10:30am, Tuesday 2:30pm-4:00pm


TA:  Giacomo Nebbia
Intelligent Systems Program
5108 Sennot Square
phone:
e-mail: gin2 at pitt edu
office hours: Monday 10:30am - noon, Tuesday 10:30am-noon



Links

Course description
Lectures
Grading
Homeworks
 

Announcements (check often)



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 AI course (CS 1571 or equivalent) or the permission of the instructor.
 

Textbook:

Stuart Russell, Peter Norvig. Artificial Intelligence.  A modern approach. 3rd ed. Prentice Hall, 2009.
Note: The third edition of the book was published at the end of 2009. Multiple changes to the first (1995) and second (2002) editions of the book occured. Please make sure you get the new edition.
 



Lectures  
 
Lectures  Topic(s)  Readings  Assignments
August 26, 2019 Administrivia. Course overview. RN - chapters 1, 2
August 28, 2019 Problem solving by searching. RN - Chapter 3.1-3.
September 4, 2019 Uninformed search methods. RN - Chapter 3.3-4. Homework assignment 1 (Programs for Hw 1)
September 9, 2019 Uninformed search methods II. RN - Chapter 3.4.
September 11, 2019 Informed search methods. RN - Chapter 3.4-5. Homework assignment 2 (Programs for Hw 2)
September 16, 2019 Constraint satisfaction search. RN - Chapter 6.
September 18, 2019 Optimal configuration search methods. RN - Chapter 4.1-2. Homework assignment 3 (Programs for Hw 3)
September 23, 2019 Methods for finding optimal configurations II.
Adversarial search.
RN - Chapter 4.1-2.,Chapter 5
September 25, 2019 Knowledge representation. Propositional logic. RN - Chapter 7. Homework assignment 4 (Programs for Hw 4)
September 30, 2019 Inference in propositional logic. RN - Chapter 7. .
October 2, 2019 Inference in propositional logic II. RN - Chapter 7. Homework assignment 5 (Programs for Hw 5)
October 7, 2019 First-order logic RN - Chapter 8 .
October 9, 2019 Inference in the first order logic. RN - Chapter 8 Homework assignment 6
October 14, 2019 First-order logic: HNF. Production systems.
Planning I: situation calculus.
RN - Chapters 8,9 and 10 (Intro and 10.4) .
October 16, 2019 Planning II: STRIPS, POP planners RN - Chapter 10 Homework assignment 7
October 21, 2019 Planning III: abstractions, contingency planers
Models of uncertainty
RN - Chapters 10, 13 .
October 23, 2019 Midterm All lectures above .
October 28, 2019 Bayesian belief networks RN - Chapters 13,14 .
October 30, 2019 Bayesian belief networks II RN - Chapters 14 Homework assignment 8
November 4, 2019 Bayesian belief networks III
Decision making in the presence of uncertainty
RN - Chapters 14,16 .
November 6, 2019 Decision making in the presence of uncertainty II RN - Chapter 16 Homework assignment 9
November 11, 2019 Machine learning RN - Chapter 18 .
November 13, 2019 Machine learning II: density estimation RN - Chapter 18 Homework assignment 10
November 18, 2019 Machine learning III: Linear regression RN - Chapter 18
November 20, 2019 Machine learning IV: Classification RN - Chapter 18 Homework assignment 11 (Programs/Data for Hw 11)
December 2, 2019 No class RN - Chapter 18 .
December 4, 2019 Machine learning V: Multilayer Neural Networks RN - Chapter 18 .
December 9, 2019 Final exam RN - Chapter 18 .



Grading

Homeworks

There will be weekly homework assignments. The homeworks will include a mix of both theoretical 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.

Collaborations. Collaborations on homeworks are not permitted.

Academic integrity. Cheating and any other antiintellectual behavior 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 understand and abide to the SCI academic integrity policy.

Programming assignments will be in Python language. Python programs submitted by you should work with Python 3.6. Please see the rules for submitting programming assignments.


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.
 



Last updated by milos on 08/26/19