Time and Place: Tuesday and Friday 9:50-11:30, Snell Library 031 and online

Khoury College of Computer Sciences

Instructor: Chris Amato

TAs listed belowThis course will introduce the student to the fundamentals of artificial intelligence including the following topics:

- Search
- Uninformed search
- Informed search
- Adversarial search
- Constraint satisfaction
- Decision making under uncertainty
- Probability refresher
- Markov Decision Processes
- Reinforcement Learning
- Graphical Models
- Bayes Networks
- Hidden Markov Models
- Machine Learning
- Supervised learning
- Unsupervised learning
- Deep learning

Artificial Intelligence: A Modern Approach, 3rd Ed., Russell and Norvig (not 4th edition yet!)

- Prereq. CS 3500.
- All programming assignments must be completed in Python. You must be willing to learn Python in order to do these assignments.
- 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.

Instructor: Chris
Amato (c.amato [at] neu.edu)

Office hours: TDB in ISEC 522 and Teams

TAs TBD

Our Piazza page is here. Please register since we'll use it for questions.

Required course work is:

- 4 Programming assignments (25% of your grade)
- Problem sets (25% of your grade)
- Quizzes (15% of your grade)
- 1 Final project and presentations (35% of your grade)

Problem sets are due at the beginning of each Tuesday class. Students may discuss the problems with other students, but must write up their own solutions.

The
final project can be on any topic related to AI. Many people
choose to work on a project applying a method studied in the class
to some practical problem. The amount of project work should be
equivalent to approximately two programming assignments. Students
my work alone, in pairs or groups of three.

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.

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%.