Syllabus

 

CS 6601

A Graduate Course in Artificial Intelligence


Fall 2015

TR 9.35am-10.55am, College of Business 100


Professor: Thad Starner

Office Hours: 6-7:30pm on Mondays TSRB 239


TA: Ian Stewart (@gatech.edu)

Office Hours: Thurs/Fri 2-4pm


TA: Stefano Fenu (@gatech.edu)

Office Hours:
Mon/Tues 2-4pm


Prerequisites

This is a graduate class. Having taken an AI class before will definitely make the class easier, but motivated students will be able to survive by self-study of the foundational material, which I will not lecture on in detail. Rather, you will be asked to review or self-study the basic material prior to each module (see below).


Communication about the class:

All communication from me will be done through T-square. Please read all announcements and email promptly.

If you want to email any of the instructors, please put “CS6601” in the subject line.


Class Goals

The desired learning outcomes for the students are:


  1. •Foundation: Having a strong foundation in AI techniques

  2. •Skills: Being able to propose, evaluate, and implement solutions to problems requiring AI techniques

  3. •Integration: Be aware of where AI intersects with other disciplines, primarily machine learning, vision, and robotics.

  4. •Assessment: Exposure to different flavors of problems and solutions, and develop a taste for some, and having confidence in how and where AI can be applied in problems relevant to society


Text

The textbook we will be using is Artificial Intelligence: A Modern Approach (AIMA, Third edition) by Stuart Russell and Peter Norvig. Note there is a much cheaper CourseSmart edition for “rent”.


Out-of-class Work

There are several activities designed to achieve the learning outcomes above:


  1. 1)Foundation, Skills, and Integration: there will be 6-8 assignments on foundational material, due at the beginning of each module (see below).

  2. 3)Assessment: The content in in-class quizzes provide a guide to the operating knowledge a researcher in AI should have when working in the field. While many detailed algorithms and processes can always be referenced in a textbook, being able to reason from principles on-the-fly is critical for discussions with colleagues.


More details will be communicated at appropriate times throughout the course, including grading criteria and standards.


Structure and Sequence of Class Activities

This course is probably different from many other courses you have taken at Georgia Tech, in that it does not follow the usual lecture pattern. Instead, while there are also conventional lectures, the course is different in two major aspects:


  1. 1)You are expected to review or study the foundational material outside class time. You will be asked to (re-)read the chapters in AIMA before the start of each module, as indicated on the schedule. Reading textbook material can be tedious, but it is necessary for you to acquire this foundation if you have not previously taken an AI class, or review it if you did. To motivate you and at the same time reward you with a grade for your hard work, an assignment based on this reading material is due the day we start with the in-depth discussions needing those foundational chapters.


  1. 2)The lectures will present advanced topics and active learning activities.


Schedule

A detailed schedule, subject to change, can be found on the schedule page.


Materials

I will be using the blackboard a lot, rather than powerpoint. Students are expected to take notes and consult the primary sources on the material, available from the website.


Collaboration Policy

Collaboration on assignments is encouraged at the "white board interaction" level. That is, share ideas and technical conversation, but write your own code. Students are expected to abide by the Georgia Tech Honor Code. Honest and ethical behavior is expected at all times. All incidents of suspected dishonesty will be reported to and handled by the office of student affairs.


Grading

Foundation, Skills, and Integration: Assignments 60%. The top N-1 of the N assignments will be counted in the final grade.

Assessment: In-class "quizzes" 40%. Class will always begin with a question or two based on the readings and on-line videos. However, these will not be graded. Instead, they are to demonstrate what I expected to be learned from the reading material and/or videos. As there is a lot of material to cover, "challenge questions" at the beginning of each section will demonstrate my focus for each unit and preview the skills I expect the student to learn in their reading. There will be a midterm and final held in class as well. They will be worth 20% each.