CS 3600 Intro to Artificial Intelligence — Spring 2020

Monday & Wednesday 4:30-5:45pm, College of Business 100.

Instructor: Brian Hrolenok
@cc.gatech.edu email: brian.hrolenok
Office Hours: 6:00pm-7:00pm, M/W, in the lobby of CODA (next door to the classroom). In response to COVID-19, office hours have been moved online. See Canvas for more information.

Course description

CS 3600 - Introduction to Artificial Intelligence is a 3-credit introductory course intended for undergraduates. Artificial Intelligence is subfield of Computer Science which covers the design, implementation, and analysis of computational systems that can be said to reason, learn, or act rationally. This course presents a broad overview of this material using an agent based approach, and has a particular focus on the details of implementation. The class is programming intensive, and a strong background in the programming language of the assignments (Python) will be very useful.

Learning objectives:

Prerequisites. The official prerequisite for this course is CS 1332, although familiarity in the following topics will be useful:

Textbook: Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, Third edition. ISBN: 978‑0136042594.
Readings for each week are given in the schedule below, and ideally should be completed before class so you can bring your questions to class.


All project submissions will be handled through Canvas, and are due by the date and time listed there. Submissions by email will not be accepted.

Late Policy

You have three free late days to be used at your discretion thoughout the semester. That means you might turn in one assignment two days late or two different assignments one day late, etc. A free late day is "used" one minute after an assignment due date. A second free late day is "used" 24 hours and one minute after the due date. A third free late day is used 48 hours and one minute after the due date. After the free late days are exhausted, you will receive a 20% penalty per day.

Project 1: This project asks you to solve a variety of search tasks, using the techniques covered in the first 4 to 5 weeks of the class. The schedule below has a tentative due date, but be sure to check on Canvas for updates.

Project 2: This project covers MDPs and Reinforcement Learning approaches to solving problems in environments with action stochasticity.

Project 3: This project covers inference and filtering in Bayesian Networks.

Project 4: This project covers Machine Learning using Decision Trees.


There will be two exams in this class, a midterm and a final (cumulative). The date for the midterm may differ from what is listed below, but the intent is for you to have as much feedback as possible before the grade-change deadline. The date for the final is fixed by the registrar. You are allowed one 8.5x11in sheet of notes, front and back. There will be no make up exam unless previously arranged (well in advance), or excused by the Dean of Students.

Grading policies

Your TAs and I will strive to provide you reasonably detailed and timely feedback on every assignment and exam. If you have any questions about any of your grades please reach out to us, either by coming to scheduled office hours or via your "@gatech.edu" email address. If there is an error with your grade, please contact us within a week of when feedback is returned, otherwise we might not be able to change it.

Point breakdown:

Academic Integrity
All of the assignments in this class are individual work only. There are absolutely solutions to some of these assignments on the Internet. Do not use them. If you can find them, so can we, and we will check. It's probably a bad idea to even look for them, even for "an idea of how to start" because once you see one solution, its hard not to think of it as the solution. If you do wind up "just looking", make sure you document it in your code. Otherwise, you run the risk of appearing to misrepresent another's work as your own. When in doubt, be explicit about where the code came from.

Being a student at Georgia Tech can be very stressful, and it's far too easy to overload your semester with difficult or time-intensive classes. When you have multiple assignments due in the same week, sometimes you have to decide how much time you can spend on each one, and sometimes there just aren't enough hours in the day. Come and talk to me about it, there's probably some way we can make things work. I'm far more willing to give extensions, hold extra office hours, and curve than I am willing to overlook violations of the honor code.


The following is the tentative schedule for the spring 2020 semester. Please check your email and Canvas regularly for any changes, as this website may not be updated immediately.

Week 1 — January 6th & 8th.
Introductions and Logistics. Reading: Ch 1-2

Week 2 — January 13th & 15th.
Uninformed search. Reading: Ch 3.1-3.4

Week 3 — January 22nd.
A* search and heuristics. Reading: Ch 3.5-3.6

Week 4 — January 27th & 29th. Project 1 due (tentative).
Search in different domains. Reading: Ch 5.1-5.3, 5.5

Week 5 — February 3rd & 5th.
Markov Decision Processes. Reading: Ch 17.1

Week 6 — February 10th & 12th.
Value Iteration and Q-learning. Reading: Ch 17.1, Ch 21.3.2

Week 7 — February 17th & 19th. Project 2 due (tentative).
Probability and state uncertainty. Reading: Ch 13

Week 8 — February 24th & 26th.
Bayes Nets. Reading: Ch 14.1-14.5

Week 9 — March 2nd & 4th.

Week 10 — March 9th & 11th. Project 3 due (tentative).
Filtering. Reading: Ch 15.1-15.3

Week 11 — March 16th — 20th.
Spring break, no class.

Week 12 — March 23rd & 25th.
Local search. Reading: Ch 4.1-4.2.

Week 13 — March 30th & April 1st.
Intro to Machine Learning. Reading: Ch 18.1, 18.2, 18.6.1, 18.6.2.

Week 14 — April 6th & 8th.
Decision Trees. Reading: Ch 18.3

Week 15 — April 13th & 15th. Project 4 due (tentative).
Neural Networks. Reading: Ch 18.7

Week 16 — April 20th.
Course summary and final review.