FAQ

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: How does Section differ from ?

A: Each semester in the Machine Learning Department, we run into the same problem: More students register for our courses than we have seats in the classroom. Some time after the Add Deadline, enough students drop the course that we are left with empty seats. Those seats could have been given to those waitlisted, but by then it's too late. As a result, many students excited about machine learning miss out.

To address this issue, we've created a "livestream" section (Section ) this semester that is identical to the other section except that the lectures are viewed at the same time online. Students in the online sections will be required to attend exams in-person and will have access to all other in-person aspects of the course (e.g. office hours). If you join , you will be a full part of the course. Here's the best part: If physical seats open up in the other sections, you will be able to join for in-person lectures too.

Can we guarantee students in Section will eventually get a seat? No. However, I expect a fair number of students will.

So if you are currently waitlisted for Section , we encourage you to sign up for Section .

Q: How do I watch the "livestream" of the lectures / recitations.

A: Click the video link on the Schedule page. (If it's not there yet, it will be soon!)

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

A: No one should be on the waitlist. Just sign up for Section (see above), which has infinite capacity. (There is a bug in the registration system that occasionally causes a waitlist on Sections . However, someone will manually add you within a week.)

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.