|
Instructor:
Dan Gildea office hours Tu/Th 11-12pm, 3019 Wegmans
TA: Nandi Zhang
Location: Tu/Th 3:25-4:40pm, 473 Hutchison
Prereqs: Data structures and algorithms.
Projects
Required text: Stuart Russell and Peter Norvig, Artificial Intelligence, A Modern Approach, 4th ed. (2020).
Syllabus
| Date | Topic | Reading |
|---|
| 9/1 |
Problem Solving |
3.0-3.3.3 |
| 9/3 |
Search Strategies |
3.3.4-3.6.1 |
| 9/8 |
Adversarial Search |
5.0-5.2.1 |
| 9/10 |
Adversarial Search 2 |
5.3-5.3.2; 5.5-5.6; 5.7 |
| 9/15 |
Local Search |
4.0-4.1 |
| 9/17 |
Local Search 2 |
4.3-4.4; 4.2, 4.5 |
| 9/22 |
Midterm 1 |
|
| 9/24 |
Constraint Satisfaction |
6.0-6.5 |
| 10/1 |
Propositional Logic |
7.0-7.4 |
| 10/6 |
Propositional Theorem Proving |
7.5 |
| 10/8 |
First Order Logic |
8.0-8.3 |
| 10/13 |
no class |
|
| 10/15 |
First Order Theorem Proving |
9 |
| 10/20 |
Midterm 2 |
|
| 10/22 |
midterm solutions |
|
| 10/27 |
Representing Uncertainty |
12.0-12.2; 12.2.3 |
| 11/3 |
Uncertain Inference |
12.3-12.7 |
| 11/5 |
Bayesian Networks |
13.0-13.2.1; 13.3-.3 |
| 11/10 |
review / problem solving |
|
| 11/12 |
Approximate Inference in Bayesian Networks |
13.4; 13.5 |
| 11/17 |
Inference in Temporal Models |
14.0-14.2; 14.3 |
| 11/19 |
Midterm 3 |
|
| 11/24 |
Learning From Examples, Decision Trees |
19.0-19.3, 19.4.0 |
| 12/1 |
Linear Regression and Linear Classifiers |
19.6, logistic regression |
| 12/3 |
Neural networks |
21.0-21.2, 21.4, 21.5.3, 21.6.1, 24.4.1 |
| 12/8 |
Learning Probabilistic Models |
20.0-20.2.2; 20.2.7 |
| 12/10 |
Learning with Incomplete Data |
20.3.0, 20.3.4 |
Final Exam: see university exam schedule
Grading
- Homeworks: 30%
- Midterms: 35%
- Final: 35%
gildea @ cs rochester edu
June 29, 2026
|