Time: MW 12-1:20pm, Eberly 332
Instructor: Milos
Hauskrecht
MIB 318, x4-8845
e-mail: milos@cs.pitt.edu
office hours: Tuesday 10-12am, Wednesday 2:30-4:30pm.
please see below for the remaining office hours
TA: Weiying Dai
MIB 317, x4-8842
e-mail: weiying@cs.pitt.edu
office hours: Tuesday, Thursday 4-5:30pm
TA: Sanjeev Dwivedi
EB 319, x4-8442
e-mail: sanjeev@cs.pitt.edu
office hours: Thursday 2:25-5:25pm
Course
description
Lectures
Grading
Homeworks
Announcements !!!!!
The final exam for the course is scheduled for Thursday,
December 13, 2001 at 8:00 - 9:50 am, EB 332.
Closed-book, covers the material for the whole semester
Office hours for M. Hauskrecht for the Final's week:
Tuesday, December 11, 2001 - 12:00 -2:00pm
Wednesday, Decemeber 12, 2001 - 2:00 -4:00pm
Graded problem set 6 will be available on December 10, 2001. Please see TA Sanjeev Dwivedi to collect your assignment 6.
Lecture notes for lectures with ppt slides are available on-line
in the .pdf format !!!
This course will provide an introduction to the fundamental concepts and techniques used in modern Artificial Intelligence and their applications.
Topics to be covered:
Textbook:
Stuart Russell,
Peter Norvig. Artificial Intelligence. A modern approach. Prentice
Hall, 1995.
| Lectures | Topic(s) | Assignments | |
|---|---|---|---|
| August 27 | Administrivia
and course overview.
Readings: RN - chapters 1, 2. |
. | |
| August 29 | Intelligent
agents. Problem solving by searching.
Readings: RN - chapters 2, 3.1-3.4. |
. | |
| September 5 | Uninformed search.
Readings: RN - chapters 3.5-3.6 |
. | |
| September 10 | Informed (heuristic)
search.
Readings: RN - chapters 4.1 - 4.3 |
Assignment 1 (due 9/24) | |
| September 12 | Constraint satisfaction
search.
Iterative improvement algorithms. Readings: RN - chapters 3.7, 4.2, 4.4 |
. | |
| September 17 | Iterative improvement
algorithms (cont).
Game search. Readings: RN - chapters 5, 20.8 |
. | |
| September 19 | Propositional logic.
Readings: RN - chapter 6 |
. | |
| September 24 | Inference in propositional
logic.
Readings: RN - chapter 6 |
Assignment 1 due
Assignment 2 out (due 10/8) C/C++ programs for assgn 2 |
|
| September 26 | First-order logic
Readings: RN - chapter 7 |
. | |
| October 1 | Inference in the first-order
logic
Readings: RN - chapter 9 |
. | |
| October 3 | Logical reasoning systems
Readings: RN - chapter 10 |
. | |
| October 8 | Planning. Situation
calculus. STRIPS.
Readings: RN - chapter 11 |
Assignment 2 due Assignment 3 out (due 10/15) |
|
| October 10 | Partial order planning.
Readings: RN - chapter 11 |
Problem set 1 solutions | |
| October 15 | Uncertainty.
Readings: RN - chapter 14 |
Problem set 3 due | |
| October 17 | Bayesian belief
networks. Readings: RN - chapter 15 |
Problem set 2 solutions Problem set 3 solutions |
|
| October 22 | Midterm exam
material covered by (including) October 10, 2001 |
. | |
| October 24 | Midterm exam solutions and results | . | |
| October 29 | Inference in Bayesian belief networks. Diagnostic inference Readings: RN - chapter 15 |
Assignment 4 out (due 11/12 !!) | |
| October 31 | Inference in Bayesian belief networks. Variable elimination. Readings: RN - chapter 15 |
. | November 5 | Inference in Bayesian belief networks. Message passing algorithm for trees. Monte-Carlo approximation. Readings: distributed during the class, RN - chapter 15 |
. | November 7 | Decision making under uncertainty
Readings: RN - chapter 16 |
. | November 12 | Decision making under uncertainty: Utility theory.
Readings: RN - chapter 16 |
Assignment 4 due Assignment 5 out (due 11/19) |
November 14 | Decision making under uncertainty: Value of
information. Learning: Introduction. Readings: RN - chapter 16, chapter 18.1-2 |
November 19 | Linear regression.
Readings: RN - chapter 19 |
Assignment 5 due Assignment 6 out (due 12/3) C/C++ programs for assgn 6 (local .pitt.edu access only) Notes for logistic regression |
| November 26 | Logistic regression.
Readings: RN - chapter 19 |
. | |
| November 28 | Multilayer neural networks. Backpropagation.
Readings: RN - chapter 19 |
. | |
| December 3 | Learning
probability distributions Course review Readings: |
Assignment 6 due Problem set solutions PS-4, PS-5 , PS-6 |
|
| December 13 | Final exam: 8:00 - 9:50am, EB 332. | . | |
The course grade will be determined roughly as follows (subject to minor modifications):
Homework assignments will include a mix of paper and pencil problems,
and programming assignments. Collaborations on homeworks are not permitted.
Homework due dates are fixed and, in general, no extensions will be granted.
To implement programming assignments you may use a programming language
of your choice. Some C/C++ code will be provided to help you implement
the assignments.