CS 4644 / 7643 Deep Learning

Spring 2026, Mon/Wed 11:00 am - 12:15 pm, Instructional Center Room 103

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 significant commercial success, billions in investment, and exciting new directions that may previously have seemed out of reach. This revolution began long ago in terms of techniques, popularized in early 2000s and especially 2012 when it began to dominate computer vision, and has now exploded because of Large Language Models (LLMs) such as ChatGPT, Gemini, Claude, and more.

This course will introduce students to the fundamentals of Neural Networks (NNs) and expose them to some cutting-edge research. It is structured in modules (background, Convolutional NNs, NN Training, Sequence Modeling, Generative Modeling, Frontiers). 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

Teaching Assistants

Head TA: David He

Aaron Liu

Haidyn Arnett

Kun-Lin Hsieh

Lawrence Zhu

Mengying Lin

Robert Azarcon

Sri Siddarth Chakaravarthy P

Woo Chul Shin

Class Info & Links

Lectures: M/W: 11:00 am - 12:15 pm, Instructional Center Room 103

Canvas (combined section) https://gatech.instructure.com/courses/498538

Piazza: Access through canvas

Gradescope: Access through canvas

Tentative Schedule (subject to changes)

Grading

Grades will consist of the following components:

• 1% Homework 0
• 48% Homework (4 homeworks each worth 12%)
• 15% In-Class Quizzes
• 36% Final Project
• 1% (potential bonus) Class Participation: top endorsed answers/questions/comments on Piazza

We will use a standard grading scheme (A: 90-100, B: 80-90, C: 70-80, D: 60-70, F: < 60)

Late policy for deliverables

• There will be no make-up work provided for missed assignments. Of course, emergencies (illness, family emergencies) will happen. In those instances, please submit an Class Absence Verification Form to Dean of Students office (see here for rules). The Dean of Students is equipped to verify emergencies and pass confirmation on to all your classes. For consistency, we ask all students to do this in the event of an emergency. Do not send any personal/medical information to the instructor or TAs; all such information should go through the Dean of Students.
• Late submission. Late submissions within 48 hours of the deadline will receive a 20% penalty. The penalty is calculated by multiplying the score after grading (including any bonus points) by 0.8. Submissions more than 48 hours late will receive a grade of 0.

In-class Quizzes (15% of course grade)

Over the course of the semester, there will be 4 in-class quizzes (top 3 of which will count), each worth up to 5% of the course grade. For dates, check the updated course schedule above. The quizzes will be administered during the usual lecture time at the usual lecture hall.

These quizzes will be conducted during class time with pen and paper, and may contain multiple choice, computation,
and short answer questions. The topics tested will be based on materials from the lectures and the assignments, with an emphasis on core concepts, computations, theory and intuitions. Notes, cheatsheets, and other aids are not allowed.

Drop-Policy

The worst quiz grade is dropped. If you are happy with the grade of your first 3 quizzes, the 4th quiz is effectively optional.

Project Details (36% 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 least the level of 1.5 homework assignment per group member (2-4 people per group). The deliverables are

- Project Proposal (2%): Due Feb 14, 2026
- Milestone Written Report (6%): Due Mar 14, 2026
- Final Written Report (28%): Due May 4, 2026

The final report is a PDF write-up describing the project in a self-contained manner will be the sole deliverable. Your final write-up is required to be between 6 - 8 pages using a standard Computer Science conference paper template such as CVPR and NeurIPS (we will release the LaTeX template). Please use this template so we can fairly judge all student projects without worrying about altered font sizes, margins, etc. After the class, we will post all the final reports online so that you can read about each others’ work. Additionally, you should upload a link to your code (to be reviewed only if there are questions of major errors in the report, team member contribution, or other extreme circumstances) and we will allow people to upload additional code, videos, and other supplementary material as zip file similar to code upload for assignments. While the PDF may link to supplementary material, external documents, and code, such resources may or may not be used to evaluate the project. The final PDF should completely address all of the points in the rubric described below.

Note that we encourage you to post your code and report on Github or other public domains after the class is over to build your portfolio, as well as submit to conferences/workshops if you choose.

Rubric

Rubrics and instructions for Project deliverables will be released on Canvas.

Prerequisites

CS 4644/7643 should NOT be your first exposure to machine learning. Ideally, you need:

• Intro-level Machine Learning
  • CS 4641 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 Python and PyTorch.
  • Your library of choice for project.
• Ability to deal with abstract mathematical concepts

Online Student Conduct and (N)etiquette

Communicating appropriately in the online classroom can be challenging. All communication, whether by Piazza, Canvas, or otherwise (including email, but please don’t use email for course communication; use Piazza), must be professional and respectful. In order to minimize this challenge, it is important to remember several points of “internet etiquette” that will smooth communication for both students and instructors

1. Read first, Write later. Read the ENTIRE set of posts/comments on a discussion board before posting your reply, in order to prevent repeating commentary or asking questions that have already been answered.
2. Avoid language that may come across as strong or offensive. Language can be easily misinterpreted in written electronic communication. Review email and discussion board posts BEFORE submitting. Humor and sarcasm may be easily misinterpreted by your reader(s). Try to be as matter of fact and as professional as possible.
3. Follow the language rules of the Internet. Do not write using all capital letters, because it will appear as shouting. Also, the use of emoticons can be helpful when used to convey nonverbal feelings. ☺
4. Consider the privacy of others. Ask permission prior to giving out a classmate’s email address or other information.
5. Keep attachments small. If it is necessary to send pictures, change the size to an acceptable 250kb or less (one free, web-based tool to try is picresize.com).
6. No inappropriate material. Do not forward virus warnings, chain letters, jokes, etc. to classmates or instructors. The sharing of pornographic material is forbidden.

NOTE: The instructor reserves the right to remove posts that are not collegial in nature and/or do not meet the Online Student Conduct and Etiquette guidelines listed above.

Plagiarism & Academic Integrity

Georgia Tech aims to cultivate a community based on trust, academic integrity, and honor. Students are expected to act according to the highest ethical standards. All students enrolled at Georgia Tech, and all its campuses, are to perform their academic work according to standards set by faculty members, departments, schools and colleges of the university; and cheating and plagiarism constitute fraudulent misrepresentation for which no credit can be given and for which appropriate sanctions are warranted and will be applied. For information on Georgia Tech’s Academic Honor Code, please visit http://www.catalog.gatech.edu/policies/honor-code/ or http://www.catalog.gatech.edu/rules/18/.

You are encouraged to discuss problems and papers with others as long as this does not involve the copying of code or solutions. After discussions, all materials that are part of a submission should be wholly your own. Do NOT search for code directly implementing the assignment and submit snippets or variations of them. You can search for conceptual information but NOT code solutions. Any public material that you use (open-source software, help from a textbook, or substantial help from a friend, etc.) should be acknowledged explicitly in anything you submit to us, but we expect that ALL code (outside of any template code we provide) should be entirely your own for the assignments. For projects, you can leverage existing code/infrastructure but should explicitely cite/mention exactly what you leveraged and which parts of the code were written by you (we expect significant contributions on your part beyond any open-source infrastructure you leverage). If you have any doubts about whether something is legal or not, please do check with the class Instructor or the TA. We will actively check for cheating, and any act of dishonesty will result in a Fail grade. Any student suspected of cheating or plagiarizing on any deliverable including assignments will be reported to the Office of Student Integrity, who will investigate the incident and identify the appropriate penalty for violations.

AI-Based Assistance

We will use the AI-based assistance policy developed by David Joyner. We treat AI-based assistance, such as ChatGPT/OpenAI Codex/Claude Code/Copilot/Cursor/etc., the same way we treat collaboration with other people: you are welcome to talk about your ideas and work with other people, both inside and outside the class, as well as with AI-based assistants. IMPORTANT: It is your responsibility to turn these tools off in any IDE you use!

However, all work you submit must be your own. You should never include in your assignment anything that was not written directly by you without proper citation (including quotation marks and in-line citation for direct quotes). Including anything you did not write in your assignment without proper citation will be treated as an academic misconduct case. Of course, we expect that all code (outside of any template code we provide) should be entirely your own for assignments. For projects, you can leverage existing code/infrastructure but should explicitely cite/mention exactly what you leveraged and which parts of the code were your own. All English text should be written by you with use of citations/quotes only to justify claims in the written reports.

If you are unsure where the line is between collaborating with AI and copying from AI, we recommend the following heuristics:

Heuristic 1: Never hit “Copy” within your conversation with an AI assistant. You can copy your own work into your conversation, but do not copy anything from the conversation back into your assignment.

Instead, use your interaction with the AI assistant as a learning experience, then let your assignment reflect your improved understanding.

Heuristic 2: Do not have your assignment and the AI agent open at the same time. Similar to above, use your conversation with the AI as a learning experience, then close the interaction down, open your assignment, and let your assignment reflect your revised knowledge.

This heuristic includes avoiding using AI directly integrated into your composition environment: just as you should not let a classmate write content or code directly into your submission, so also you should avoid using tools that directly add content to your submission.

Deviating from these heuristics does not automatically qualify as academic misconduct; however, following these heuristics essentially guarantees your collaboration will not cross the line into misconduct.

Students with Disabilities

If you are a student with learning needs that require special accommodation, contact the Office of Disability Services at 404.894.2563 or http://disabilityservices.gatech.edu/, as soon as possible, to make an appointment to discuss your special needs and to obtain an accommodations letter. Please also e-mail me as soon as possible in order to set up a time to discuss your learning needs.

Subject to Change Statement

The syllabus and course schedule may be subject to change. Changes will be communicated via the Piazza. It is the responsibility of students to check Piazza, email messages, and course announcements to stay current in their online courses.

Campus Resources

Community Resources

FAQs

• What should I do for HW0?

  HW0 is short introductory assignment for you to self-assess your readiness for the course and familiarize yourself with the Gradescope submission process. The materials are available here: HW0. HW0 is due Monday 1/19/2026 11:59pm, so as to allow sufficient time for students to be added to Gradescope and submit the assignment.

• 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 HW0. However, you should still complete the assignment and submit it to Gradescope after you are enrolled in the course.

• 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 make a post on Piazza.

• I am graduating this Spring 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.

• 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, create a private piazza post.

Related Classes / Online Resources

• CS231n Convolutional Neural Networks for Visual Recognition, Stanford
• Machine Learning, Oxford
• Deep Learning, New York University
• Deep Learning, CMU
• Deep Learning, University of Maryland

Book

• Deep Learning, Ian Goodfellow, Aaron Courville, and Yoshua Bengio, MIT Press

Overviews

• Deep Learning, Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, Nature
• Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, and Pascal Vincent

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.