CS 5100/4100: Foundations of Artificial Intelligence (Spring 2018)

Brief Course Description

This course will introduce the student to the fundamentals of artificial intelligence including the following topics:

1. Search
• Uninformed search
• Informed search
• Adversarial search
• Constraint satisfaction
2. Reasoning about uncertainty
• Introduction to Decision Theory
• Probability, Bayesian Probability
• Bayesian Networks
3. Decision-making
• Utility Theory
• Markov Decision Processes
4. Learning to act
• Reinforcement Learning
• Machine Learning
5. Current Topics
• Affective Computing and Virtual Humans

The course schedule is subject to change. See the schedule tab above.

Textbook

Artificial Intelligence: A Modern Approach, 3rd Ed., Russell and Norvig

Prerequisites

1. Prereq. CS 2800 and CS 3500.
2. All programming assignments must be completed in Python. You must be willing to learn Python in order to do these assignments.
3. The course will require you to use basic probability and linear algebra. If you do not have this background, you must be willing to learn it as we go.

Academic Integrity

Cheating and other acts of academic dishonesty will be referred to OSCCR (office of student conduct and conflict resolution) and the College of Computer Science. See this link.

Lateness Policy

Late programming assignments will be penalized by 10% for each day late. For example, if you turned in a perfect programming assignment two days late, you would receive an 80% instead of 100%.

Instruction Staff

Instructor: Stacy Marsella ( s [dot] marsella [at] neu [dot] edu )
Office hours: Fridays, 2-4, 302E West Village H, or by Appt.

TA: Kevin Shah, shah.kevi@husky.neu.edu
Office hours: