Brief Course Description

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

  1. Search
    • classical search
    • optimization
    • adversarial search / game playing
    • constraint satisfaction
  2. Introduction to logic and planning
  3. Reasoning under uncertainty
    • Bayesian representations
    • exact and approximate inference
    • decision theory
  4. Machine learning
    • Linear classification
    • Nonparameteric learning
    • Statisical learning / Expectation Maximization

The course schedule is subject to change. See the schedule tab above. Also, please check out the course syllabus.


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


CS 2800 and CS 3500.

Academic Integrity

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


The final exam will be available at noon on 12/11/2013 here and will be due at 5pm EST on 12/13/2013 in SVN.

We will have a take-home exam during exam period. You do not need to by physically present at Northeastern in order to take the exam. You will have a window of a two or three days over which to complete the exam.

Instruction Staff

Primary Instructor: Robert Platt ( r [dot] platt [at] neu [dot] edu )
Office hours: Friday, 10:30am -- 12:00, 208B West Village H

TA: Maryam Aziz
Office hours: Monday 8am - 10am,, 472 West Village H

TA: Yupeng Gu
Office hours: 9:30 to 11:30 am on Wednesday,, 208 West Village H

Work Load

Required course work includes:

  • Homework assignments (50% of your grade)
  • In class quizzes (10% of your grade)
  • Midterm exam (20% of your grade)
  • Final exam (20% of your grade)

In-class Quizzes

Most classes will begin with a short quiz that checks that you have read the material assigned for that day. I will drop your two lowest quiz grades. If you miss a class, then you will receive a zero for the corresponding quiz.


There will be approximately five homeworks assigned throughout the semester. Most of these assignments will include a programming component (in Python). Homeworks are due at 5pm on the due date. Late assignments will be penalized by 10% for each day late. For example, if you turned in a perfect homework assignment two days late, you would receive an 80% instead of 100%.