Computer Science 242

Artifical Intelligence

Instructor: Ted Pawlicki office hours on instructor website.

TAs:

Location: Tu/Th 3:25pm-4:40pm, Wegmans 1400

Prereqs: Data structures and algorithms.

Study sessions:

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.

Date	Topic	Reading
8/27	Problem Solving	3.0-3.3.3
8/29	Search Strategies	3.3.4-3.6.1
9/3	Adversarial Search	5.0-5.2.1
9/5	Adversarial Search 2	5.3-5.3.2; 5.5-5.6; 5.7
9/10	Local Search	4.0-4.1
9/12	Local Search 2	4.3-4.4; 4.2, 4.5
9/17	Midterm 1
9/19	midterm solutions
9/24	Constraint Satisfaction	6.0-6.5
9/26	Propositional Logic	7.0-7.4
10/1	Propositional Theorem Proving	7.5
10/3	First Order Logic	8.0-8.3
10/8	First Order Theorem Proving	9
10/10	Midterm 2
10/15	no class
10/17	midterm solutions
10/22	Representing Uncertainty	12.0-12.2; 12.2.3
10/24	Uncertain Inference	12.3-12.7
10/29	Bayesian Networks	13.0-13.2.1; 13.3-.3
10/31	Approximate Inference in Bayesian Networks	13.4; 13.5
11/5	Inference in Temporal Models	14.0-14.2; 14.3
11/7	review / problem solving
11/12	Learning From Examples, Decision Trees	19.0-19.3, 19.4.0
11/14	Linear Regression and Linear Classifiers	19.6, logistic regression
11/19	Neural networks	21.0-21.2, 21.4, 21.5.3, 21.6.1, 24.4.1
11/21	Learning Probabilistic Models	20.0-20.2.2; 20.2.7
11/26	Learning with Incomplete Data	20.3.0, 20.3.4
11/28	no class
12/3	review / problem solving
12/5	Review

Final Exam: Monday, December 16, 8:30am-11:30am, 1400 Wegmans.

pawlicki @ cs rochester edu

August 29, 2024