Frequently Asked Questions

Q: I just found this website, what should I do next?

A: Please read through this FAQ and the Syllabus page. If you are registered (or waitlisted) for the course, the course staff will enroll you in the technologies we will use for communication (Piazza) and homework assignment submission (Gradescope). If it is after the first day of class, you have been registered for more than two days, and you still don’t have access to one of these, then go ahead and enroll yourself in Piazza using your Andrew Email and send a “Private Note” to the instructors that includes your Andrew ID.

Q: Will I be able to get off the waitlist?

A: Of course, we can never guarantee that you will be able to get off the waitlist. However, we expect the waitlist to clear a couple weeks into the semester. If you submit all the homeworks and keep up with the work, there is a reasonable chance you’ll be able to get in. Originally, we had an 80 person room, but when 300+ students tried to register we were given a 240 person room. Since many students sign up for many more courses than they actually expect to take, we anticipate that the number registrants to drop below our seating capacity soon after classes begin. Please be patient and kindly do not message the course staff asking for special treatment regarding the waitlist.

Q: Does this course fulfill an academic requirement for me?

A: As of the start of Spring 2024, this course does fulfill an academic requirement for the Master’s in Machine Learning (MSML), the AI major, the ML Minor, and the ML Concentration. It may fulfill an academic requirement for your program, and the best way to find out is to ask your program director.

Q: Why was this course created?

A: Students interested in generative AI can already access most of the important methods driving the recent growth in the field. However, to do so they might need to take four to five courses in MLD (e.g. an undergrad could take 10-417, 10-403, 10-414, 10-405, 10-425) or a variety from LTI and RI. The purpose of this course is to provide a single course that brings all of these topics together under one roof. In doing so, we will also be able to draw ties between the different methods and how they interact.

Q: Can I learn about generative AI in other courses?

A: Certainly! Here’s a very incomplete list…

11-667 Large Language Models: Methods and Applications: Explores the models, optimization methods, and training regimes that are driving the current advancements in LLMs.
16-726 Learning-Based Image Synthesis: Covers a variety of ML techniques for image synthesis (aka. generation) and includes many of the modern models and techniques driving the field.
16-824 Visual Learning and Recognition: Considers an array of computer vision applications besides just generation and state-of-the-art models for them.
10-414/714 Deep Learning Systems: Covers aspects of how to build efficient scalable systems, such as those for LLMs.
10-403 Deep Reinforcement Learning: Covers reinforcement learning techniques that are important for understanding RLHF used to fine-tune LLMs for chat.
10-417/617 Intermediate Deep Learning: Covers the basics architectures and methods used by generative AI, such as Transformers.
10-405/605 Machine Learning for Large Datasets: Covers aspects of how to scale up machine learning to massive datasets and distributed learning across many machines.
10-425/625 Introduction to Convex Optimization: Covers modern optimization techniques used to train foundation models.