Course Overview

Description: Fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, 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-fl2025@mit.edu, and not to the instructors directly.

Course Information

Class Schedule

** class schedule is subject to change **

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.

AI assistants policy

• Our policy for using ChatGPT and other 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).

Attendance policy

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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Previous years

Fall 2024
Fall 2023
Fall 2022
Fall 2021

 

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