MIT EECS 6.7960 Deep Learning

Fall 2026

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Course Overview

Description: Fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high-dimensions, and applications to computer vision, natural language processing, and robotics.

Pre-requisites: 18.05 and (6.3720, 6.3900, or 6.C01)

Note: This course is appropriate for advanced undergraduates and graduate students, and is 3-0-9 units. Due to heavy enrollment, we will very unfortunately not be able to take cross-registrations this semester.

Any and all personal or logistical questions, such as regarding absenses, accomodations, etc should be emailed to the course email, 6.7960-instructors@mit.edu, and not to the instructors directly.

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Course Information

 

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Class Schedule

** class schedule is subject to change **

Date	Topics	Speaker	Course Materials	Assignments
Week 1
Thu 9/10	Course overview, introduction to deep learning	Kaiming He	Slides Optional Reading: Notation for this course Neural Networks
Week 2
Tue 9/15	How to train a neural net	Phillip Isola	Slides Required Reading: Gradient-Based Learning Backprop Optional Reading: Old Optimizer, New Norm: An Anthology	pset 1 out
Thu 9/17	Approximation theory	Phillip Isola	Slides Interactive Demo Optional Reading: Deep learning theory notes sections 2 and 5 (this is written at a rather advanced level; try to get the intuitions rather than all the details)
Thu 9/17	PyTorch Tutorial: 6:00-7:00 PM, 34-101	Aryan Bora	Pytorch Tutorial Colab
Week 3
Tue 9/22	Architectures: ConvNets	Kaiming He	Slides Required Reading: CNNs Optional Reading: CS231n: Convolutional Neural Networks
Tue 9/22	Agentic Coding Tutorial: 6:00-7:00 PM, 32-123	Anakha Ganesh	GitHub Slides
Thu 9/24	Architectures: Memory and Sequence Modeling	Kaiming He
Thu 9/24	Agentic Coding Tutorial: 6:00-7:00 PM, 32-123	Suraj Reddy
Week 4
Tue 9/29	Architectures: Transformers	Phillip Isola		pset 1 due pset 2 out
Thu 10/1	Generalization Theory	Phillip Isola
Week 5
Tue 10/6	Going Deep with Neural Networks	Kaiming He
Thu 10/8	Representation Learning Methods	Kaiming He
Week 6
Tue 10/13	No class — Monday schedule			pset 2 due pset 3 out
Thu 10/15	Representation Learning: Weight-Space Geometry	Phillip Isola
Week 7
Tue 10/20	Representation Learning: Information Theory	Kaiming He
Thu 10/22	Foundation models: pre-training	Phillip Isola
Week 8
Tue 10/27	Foundation models: scaling laws	Phillip Isola		pset 3 due pset 4 out
Thu 10/29	Midterm
Week 9
Tue 11/3	Generative models: basics	Kaiming He
Thu 11/5	Generative models: VAE and GAN	Kaiming He
Week 10
Tue 11/10	Generative models: Diffusion and Flows	Kaiming He		pset 4 due pset 5 out
Thu 11/12	Generalization (OOD)	Phillip Isola
Week 11
Tue 11/17	Transfer learning: Models and Data	Phillip Isola
Thu 11/19	Inference-time Algorithms	Phillip Isola
Week 12
Tue 11/24	Guest Lecture 1	TBD
Thu 11/26	No class — Thanksgiving
Week 13
Tue 12/1	Evaluation	Phillip Isola		pset 5 due
Thu 12/3	Applying Deep Learning to Your Problems	Kaiming He
Week 14
Tue 12/8	Guest Lecture 2	TBD
Thu 12/10	Guest Lecture 3	TBD
Week 15
Tue 12/15	Final Exam

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Collaboration policy

• Psets should be written up individually and should reflect your own individual work. However, you may discuss with your peers, TAs, and instructors.
• You should not copy or share complete solutions or ask others if your answer is correct (in person or via piazza/canvas).
• If you work with anyone on the pset (other than TAs and instructors), list their names at the top of the pset.

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AI assistants policy

• Our policy for using AI assistants is identical to our policy for using human assistants.
• This is a deep learning class and you should try out all the latest AI assistants (they are pretty much all using deep learning). It's very important to play with them to learn what they can do and what they can't do. That's a part of the content of this course.
• Just like you can come to office hours and ask a human questions (about the lecture material, clarifications about pset questions, tips for getting started, etc), you are very welcome to do the same with AI assistants.
• But: just like you are not allowed to ask an expert friend to do your homework for you, you also should not ask an expert AI.
• If it is ever unclear, just imagine the AI as a human and apply the same norm as you would with a human.
• If you work with any AI on a pset, briefly describe which AI and how you used it at the top of the pset (a few sentences is enough).

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Attendance policy

Attendance is at your discretion. Recordings will be released here right after each class.

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Late policy

• Homeworks will not be accepted more than 7 days after the deadline.
• The grade on a homework received n days after the deadline (n<=7) will be multiplied by (1-n/14). We will round up to units of full days; submitting 1 hour late counts as using 1 late day.
• Ten penalty days will be automatically waived for each student.

For example, let's say a student perfectly solves the first three homeworks but submits the first homework 8 days late, the second homework six days late and the third homework five days late. Then the student will score zero on the first homework, 100% on the second homework (using up six late days) and 100% * (1-1/14) = 92.9% on the third homework (using up the last four remaining late days).

The slack days are meant to be used for all the normal circumstances of life: being behind on work, forgetting the deadline, having a conference to attend, etc. We will not grant further extensions for these routine issues. For any extension request (i.e. serious medical issues or life events) please contact S3 (for undergrads) or GradSupport (for grad students) and we will work with them to find a good solution.
• We will not be able to support course incompletes.

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Office Hours

Office hours are listed on the calendar below. You can also add this calendar to your own Google Calendar.

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Previous years

Fall 2025
Fall 2024
Fall 2023
Fall 2022
Fall 2021

 

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