University of Massachusetts Amherst
College of Information and Computer Sciences


Graduate Artificial Intelligence

Spring 2019

Course Description:

This course will provide an overview of concepts and techniques in the area of classical Artificial Intelligence. See class schedule below.

Lectures: Tuesday & Thursday 10:00-11:15 in South College W245

Credit: 3 units


Teaching assistants:




Class materials will be posted to the Moodle course.

Discussions will happen on the Google group or over Moodle.

Class schedule (may be subject to change):

Weekday Date Title References.
Textbook reading is mandatory, the papers are optional.
Tuesday 1/22/2019 Introduction AIMA Ch. 1 & 2;
Horvitz et al. "Decision theory in expert systems and artificial intelligence"
Hassabis et al. "Neuroscience-Inspired Artificial Intelligence"
Gharamani "Probabilistic machine learning and artificial intelligence"
Scholkopf "Learning to see and act"
Thursday 1/24/2019 Problem Solving as Search. Heuristics. AIMA Ch. 3
Tuesday 1/29/2019 Abstraction and Hierarchical Search -
Thursday 1/31/2019 Local Search AIMA Ch. 4
Tuesday 2/5/2019 Resource-bounded Search Techniques -
Thursday 2/7/2019 Adversarial Search AIMA Ch. 5
Tuesday 2/12/2019 Constraint Satisfaction -
Thursday 2/14/2019 Constraint Satisfaction (continued) AIMA Ch. 6
Tuesday 2/19/19 No class [Monday schedule]
Thursday 2/21/2019 Principles of Knowledge Representation and Reasoning
Tuesday 2/26/2019 Inference in Propositional Logic AIMA Ch. 7
Thursday 2/28/2019 Inference in First Order Logic AIMA Ch. 8 & 9
Tuesday 3/5/2019 Probabilistic Reasoning using Bayesian Networks -
Thursday 3/7/2019 Exact Inference in Belief Networks. AIMA Ch. 13
Tuesday 3/12/2019 No class [Spring break]
Thursday 3/14/2019 No class [Spring break]
Tuesday 3/19/2019 Approximate Inference in Belief Networks. [Guest Lecture] AIMA Ch. 14
Thursday 3/21/2019 Project pitches or midterm. [May be swapped with the 19th.]
Tuesday 3/26/2019 Decision Theory -
Thursday 3/28/2019 Decision Networks AIMA Ch. 16
Tuesday 4/2/2019 Planning AIMA Ch. 10
Thursday 4/4/2019 Planning under Uncertainty AIMA Ch. 11
Tuesday 4/9/2019 POMDPs TBA
Thursday 4/11/2019 Reinforcement Learning AIMA Ch. 21
Tuesday 4/16/2019 Deep Reinforcement Learning TBA
Thursday 4/18/2019 Multi-agent Systems TBA
Tuesday 4/23/2019 Advanced Topic I TBA
Thursday 4/25/2019 Advanced Topic II TBA
Tuesday 4/30/2019 Recap Sample exam questions

Exam exception policy: If you have any special needs/circumstances pertaining to an exam, you must talk to the instructor before the exam.

Late homework policy: If you cannot turn in a homework on time, you will need to discuss with the instructor at least one day in advance.

Regrade policy: Any requests for regrading must be submitted within a week of receiving the grade and preferably discussed during office hours. Each TA will be responsible for a different part of the homework, as indicated when the assignment is issued, so please direct questions appropriately. Only contact the instructor after discussing the issue with the TAs.

Copyright/distribution notice: Many of the materials created for this course are the intellectual property of the course creator, Shlomo Zilberstein and the course instructor, Ina Fiterau. This includes, but is not limited to, the syllabus, lectures and course notes. Except to the extent not protected by copyright law, any use, distribution or sale of such materials requires the permission of the instructor. Please be aware that it is a violation of university policy to reproduce, for distribution or sale, class lectures or class notes, unless copyright has been explicitly waived by the faculty member.