CS 3600 Intro to Artificial Intelligence — Spring 2019

Monday & Wednesday & Friday 11:15pm-12:05pm, College of Business room 100

Instructor: Brian Hrolenok
@cc.gatech.edu email: brian.hrolenok
Office: TSRB 241
Office Hours: M/F 12:30pm-1:30pm (and by appointment)

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. Canvas should be configured to allow submissions up to an hour late for most assignments, but these will incur a 10% penalty, and late submissions may be disabled if abused. Submissions by email will not be accepted.

Project 0: This ungraded project will be due in the first few weeks of course. It will provide an example of how our autograder system works, and give you an idea of what to expect. If many of the concepts presented are new to you, you'll need to work extra hard on future projects.

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 with 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 feedback before the grade-change deadline. The date for the final is fixed by the registrar. Both exams are closed-book, closed-notes 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 2019 semester. Please check your email and Canvas regularly for any changes, as this website may not be updated immediately.

Week 1 — January 7th & 9th & 11th.
Introductions and Logistics. Reading: Ch 1-2.

Week 2 — January 14th & 16th & 18th. Project 0 due.
Search (part 1). Reading: Ch 2-3.

Week 3 — January 23rd & 25th.
Search (part 2). Reading: Ch 3.

Week 4 — January 28th & 30th & February 1st.
Search (part 3). Reading: Ch 3.

Week 5 — February 4th & 6th & 8th. Project 1 due (tentative).
Markov Decision Processes. Reading: Ch 17.1-17.3.

Week 6 — February 11th & 13th & 15th.
Q-learning. Reading: Ch 21.3.2.

Week 7 — February 18th & 20th & 22st.
Probability review. Reading: Ch 13.

Week 8 — February 25th & 27th & March 1st.
Bayes nets. Reading: Ch 14.

Week 9 — March 4th & 6th & 8th.
Midterm review. Summarizing the course so far, reviewing important concepts, and preparing for the exam this week. Midterm will be on Friday, March 8th, during the normal class period.

Week 10 — March 11th & 13th & 15th. Project 2 due (tentative).
Partially Observable MDPs. Reading: Ch 17.4.

Week 11 — March 18th — 22nd
Spring break — no class.

Week 12 — March 25th & 27th & 29th
Optimization. Reading: Ch 4.

Week 13 — April 1st & 3rd & 5th. Project 3 due (tentative).
Decision Trees. Reading: Ch 18.

Week 14 — April 8th & 10th & 12th
Neural Networks. Reading: Ch 18.

Week 15 — April 15th & 17th & 19th
Additional topics.

Week 16 — April 23rd. Project 4 due by 11:59pm EDT on Sunday April 21st.
Course summary and final review.