Instructor:
Ted Pawlicki office hours on instructor website.
TAs:
- Yolo Tang ytang37@UR.rochester.edu
- Pai Liu pliu29@UR.rochester.edu
- Junjie Zhao jzhao58@u.rochester.edu
Location: Tu/Th 3:25pm-4:40pm, Wegmans 1400
Prereqs: Data structures and algorithms.
Homework Projects
Required text: Stuart Russell and Peter Norvig, Artificial Intelligence, A Modern Approach, 4th ed. (2020).
If you want to add the class, complete the first project, and
then email it to pawlicki@cs along with a note including your
year and major.
Syllabus
Date | Topic | Reading |
8/26 |
Problem Solving |
3.0-3.3.3 |
8/28 |
Search Strategies |
3.3.4-3.6.1 |
9/2 |
Adversarial Search |
5.0-5.2.1 |
9/4 |
Adversarial Search 2 |
5.3-5.3.2; 5.5-5.6; 5.7 |
9/9 |
Local Search |
4.0-4.1 |
9/11 |
Local Search 2 |
4.3-4.4; 4.2, 4.5 |
9/16 |
Midterm 1 |
|
9/18 |
midterm solutions |
|
9/23 |
Constraint Satisfaction |
6.0-6.5 |
9/25 |
Propositional Logic |
7.0-7.4 |
10/30 |
Propositional Theorem Proving |
7.5 |
10/2 |
First Order Logic |
8.0-8.3 |
10/7 |
First Order Theorem Proving |
9 |
10/9 |
Midterm 2 |
|
10/14 |
no class |
|
10/16 |
midterm solutions |
|
10/21 |
Representing Uncertainty |
12.0-12.2; 12.2.3 |
10/23 |
Uncertain Inference |
12.3-12.7 |
10/27 |
Bayesian Networks |
13.0-13.2.1; 13.3-.3 |
10/30 |
Approximate Inference in Bayesian Networks |
13.4; 13.5 |
11/4 |
Inference in Temporal Models |
14.0-14.2; 14.3 |
11/6 |
review / problem solving |
|
11/11 |
Learning From Examples, Decision Trees |
19.0-19.3, 19.4.0 |
11/13 |
Linear Regression and Linear Classifiers |
19.6, logistic regression |
11/18 |
Neural networks |
21.0-21.2, 21.4, 21.5.3, 21.6.1, 24.4.1 |
11/20 |
Learning Probabilistic Models |
20.0-20.2.2; 20.2.7 |
11/25 |
Learning with Incomplete Data |
20.3.0, 20.3.4 |
11/27 |
no class |
|
12/2 |
review / problem solving |
|
12/4 |
Review |
|
Final Exam: Monday, December 15, 8:30am-11:30am, 1400 Wegmans.
Grading
- Homeworks: 40%
- Midterms: 25%
- Final: 35%
pawlicki @ cs rochester edu
August 19, 2025
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