Course Meeting Times
Lectures: 2 sessions/week; 1.5 hours/session
Prerequisites
Students in this course should have previously taken 18.05 Introduction to Probability and Statistic and one of the following three courses: 6.3720 Introduction to Statistical Data Analysis, 6.3900 Introduction to Machine Learning, or 6.C01 Modeling with Machine Learning: from Algorithms to Applications.
Course Description
This course covers the 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.
Grading
65% Problem sets
35% Final project
Collaboration Policy
- Problem sets 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. If you work with anyone on the problem set (other than TAs and instructors), list their names at the top.
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 problem set questions, tips for getting started, etc.), you are very welcome to do the same with AI assistants.
- But just as 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 problem set, briefly describe which AI and how you used it at the top of the problem set. (A few sentences is enough.)
