CS 4803 / 7643 Deep Learning

Spring 2020, TR 1:30 - 2:45 pm, Location TBD

Course Information

This is an exciting time to be studying (Deep) Machine Learning, or Representation Learning, or for lack of a better term, simply Deep Learning!

Deep Learning is rapidly emerging as one of the most successful and widely applicable set of techniques across a range of domains (vision, language, speech, reasoning, robotics, AI in general), leading to some pretty significant commercial success and exciting new directions that may previously have seemed out of reach.

This course will introduce students to the basics of Neural Networks (NNs) and expose them to some cutting-edge research. It is structured in modules (background, Convolutional NNs, Recurrent NNs, Deep Reinforcement Learning, Deep Structured Prediction). Modules will be presented via instructor lectures and reinforced with homeworks that teach theoretical and practical aspects. The course will also include a project which will allow students to explore an area of Deep Learning that interests them in more depth.

Instructor: Zsolt Kira




Class meets
Tuesday, Thursday 1:30 - 2:45 pm

Piazza
To be announced!
Canvas
To be announced!
Gradescope
To be announced!

Schedule

To be announced.

Grading

  • 80% Homework (4 homeworks)
  • 20% Final Project

Late policy for deliverables

  • No penalties for medical reasons or emergencies. Please see GT Catalog for rules about contacting the office of the Dean of Students.
  • Every student has 7 free late days (7 x 24-hour chunks) for this course.
  • After all free late days are used up, penalty is 25% for each additional late day.

Prerequisites

CS 4803/7643 should not be your first exposure to machine learning. Ideally, you need:

  • Intro-level Machine Learning
    • CS 3600 for the undergraduate section and CS 7641/ISYE 6740/CSE 6740 or equivalent for the graduate section.
  • Algorithms
    • Dynamic programming, basic data structures, complexity (NP-hardness)
  • Calculus and Linear Algebra
    • positive semi-definiteness, multivariate derivates (be prepared for lots and lots of gradients!)
  • Programming
    • This is a demanding class in terms of programming skills.
    • HWs will involve a mix of languages (Python, C++) and libraries (PyTorch).
    • Your language of choice for project.
  • Ability to deal with abstract mathematical concepts

Project Details (20% of course grade)

The class project is meant for students to (1) gain experience implementing deep models and (2) try Deep Learning on problems that interest them. The amount of effort should be at the level of one homework assignment per group member (1-5 people per group).

Rubrik (60 points)

To be announced

FAQs

  • The class is full. Can I still get in?

    Sorry. The course admins in CoC control this process. Please talk to them.

  • Unregistered Students who intend to register:

    If you are not registered for this course, you will not have access to gradescope for submission of PS0. Please fill the following form in order to be added to gradescope and be able to submit PS0: https://forms.gle/sG8vB1vayXzgsKAA9

    Students who individually emailed us and have not been added yet - you may have left out the details of which course instance you are planning to take (either CS 7643 or CS 4803). There are two separate gradescope courses for the two instances. Please fill the above form in order to provide us with this information.

  • Registered students who are not able to access gradescope:

    This will happen if you were registered to the course very recently. Gradescope rosters are synced periodically and it may take some time for you to receive a gradescope sign-up notification. If you still face problems with accessing gradescope, please post a comment below.

  • I am graduating this Fall and I need this class to complete my degree requirements. What should I do?

    Talk to the advisor or graduate coordinator for your academic program. They are keeping track of your degree requirements and will work with you if you need a specific course.

  • Can I audit this class or take it pass/fail?

    No. Due to the large demand for this class, we will not be allowing audits or pass/fail. Letter grades only. This is to make sure students who want to take the class for credit can.

  • Can I simply sit in the class (no credits)?

    In general, we welcome members of the Georgia Tech community (students, staff, and/or faculty) to sit-in. Out of courtesy, we would appreciate if you let us know beforehand (via email or in person). If the classroom is full, we would ask that you please allow registered students to attend.

  • I have a question. What is the best way to reach the course staff?

    Registered students – your first point of contact is Piazza (so that other students may benefit from your questions and our answers). If you have a personal matter, email us at the class mailing list f19-cs4803-cs7643-staff@googlegroups.com

Related Classes / Online Resources

Book

Overviews

Note to people outside Georgia Tech

Feel free to use the slides and materials available online here. If you use our slides, an appropriate attribution is requested. Please email the instructor with any corrections or improvements.