Computer Science 242

Artifical Intelligence

Summer 2025

Instructor: Ted Pawlicki office hours on instructor website.

This course is fully online and fully asychronous. Recorded lectures are posted on Blackboard. Reading is manditory. Not everything in the book is covered in lecture. Not everything covered in lecture is in the book. You are responsible for both.

Prereqs: Data structures and algorithms.

Homework Projects . These have strict deadlines.

Required text: Stuart Russell and Peter Norvig, Artificial Intelligence, A Modern Approach, 4th ed. (2020).

Syllabus

The lectures are recorded and posted on Blackboard. The dates are approximate. Since the course is asychronous there is flexibility in when you view the lectures. Exams are on specific days.

DateTopicReading
5/19 Problem Solving 3.0-3.3.3
5/21 Search Strategies 3.3.4-3.6.1
5/24 Adversarial Search 5.0-5.2.1
5/26 Adversarial Search 2 5.3-5.3.2; 5.5-5.6; 5.7
5/29 Local Search 4.0-4.1
5/31 Local Search 2 4.3-4.4; 4.2, 4.5
6/1 Homework 1 Due
6/4 Constraint Satisfaction 6.0-6.5
6/7 Propositional Logic 7.0-7.4
6/10 Propositional Theorem Proving 7.5
6/13 First Order Logic 8.0-8.3
6/15 Homework 2 Due
6/18 First Order Theorem Proving 9
6/20 Midterm 1
6/23 midterm solutions
6/25 Representing Uncertainty 12.0-12.2; 12.2.3
6/28 Uncertain Inference 12.3-12.7
6/29 Homework 3 Due
6/30 Bayesian Networks 13.0-13.2.1; 13.3-.3
7/2 Approximate Inference in Bayesian Networks 13.4; 13.5
7/4 Inference in Temporal Models 14.0-14.2; 14.3
7/6 Homework 4 Due
7/8 Learning From Examples, Decision Trees 19.0-19.3, 19.4.0
7/11 Midterm 2
7/14 midterm solutions
7/17 Linear Regression and Linear Classifiers 19.6, logistic regression
7/20 Homework 5 Due
7/24 Neural networks 21.0-21.2, 21.4, 21.5.3, 21.6.1, 24.4.1
7/27 Learning Probabilistic Models 20.0-20.2.2; 20.2.7
7/30 Learning with Incomplete Data 20.3.0, 20.3.4
8/1 Final Exam

Grading

  • Homeworks: 40%
  • Midterms: 25%
  • Final: 35%

pawlicki @ cs rochester edu
May 10, 2025