University of Massachusetts Amherst
College of Information and Computer Sciences

 

 

COMPSCI 383

Artificial Intelligence

   Fall 2017

 

Ian Gemp

 

Course Information Tentative Schedule

Depending on course progress, potential snow days, and other unforeseen circumstances, this schedule is subject to change at the discretion of the instructors.
Date Topic Reading 3rd Edition
9/6 Introduction 1.1; Fig 1.2,1.3; 1.5; 2.1-2.5
9/11 Problem Solving as Search 3.1-3.4
9/13 Heuristic Search 3.5-3.6
9/18 Local Search 4.1, 4.3
9/20 Constraint Satisfaction 6.1-6.2
9/25 Constraint Satisfaction (continued) 6.3-6.5
9/27 Adversarial Search and Game Playing 5.1-5.4
10/2 Logical Agents and Propositional Logic 7.1-7.4
10/4 Inference in Propositional Logic 7.5-7.7
10/10 First-Order Logic + Inference 8.1-8.3, 9.1-9.5
10/11 Uncertainty + Probability 13.1-13.6
10/16 Review for Midterm
10/18 Midterm (in class)
10/23 Bayesian Networks 14.1-14.3
10/25 Exact and Approximate Inference in Bayesian Networks 14.4-14.6
10/30 Maximum Expected Utility Principle 16.1-16.4
11/1 Sequential Decision Making 17.1-17.4
11/6 Reinforcement Learning 21.1-21.6
11/8 Reinforcement Learning 21.1-21.6
11/13 Machine Learning 18.6, 18.8-18.12
11/15 Machine Learning 20.1-20.3
11/27 Neural Networks 18.7
11/29 Neural Networks 18.7
12/4 Special Topics ?
12/6 Final Review
12/11 Final Review
12/15 Final (3:30-5:30PM in ILC S131)

(*) Significant differences between 2nd and 3rd editions


© 2016 Shlomo Zilberstein, Spring 2017 Richard Freedman and Kristina Fedorenko, modified Fall 2017 by Ian Gemp with permission.