Introduction to Machine Learning

10-301 + 10-601, Spring 2024
School of Computer Science
Carnegie Mellon University

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: How does 10-301 differ from 10-601?

A: Undergraduates must register for 10-301 and graduate students must register for 10-601. Otherwise, the courses will be identical in all respects.

Q: How does Section A differ from Section B?

A: This semester, Section A and Section B will only differ in the time / location (depending on the locations) of the lectures. Everything else will be the same, including the instructor, course content, homeworks, exams, policies, etc.

Q: Can I watch the “livestream” of the lectures / recitations?

A: This is an in-person course and we expect you to be in-person! However, we also understand that circumstances might sometimes make it difficult for you to make it to the classroom. For those cases, at least one section will be livestreamed and recorded and, regardless of which section you are registered for, you may join via that livestream.

Lectures and recitations will be livestreamed via Zoom. The Zoom link is available on Piazza. The recordings will be available several hours afterwards via Panopto. To access the recordings: Click the “Video Recordings” link on the “Links” dropdown. Log in with your Andrew ID.

The 12:30 PM lecture/recitation will be livestreamed and recorded. The 11:00 AM lecture/recitation will not.

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

A: No one should be on the waitlist after the first week of classes. We plan to ensure that everyone who wants to register for the course is able to. There is typically a drop of about 10% in the first week since some people sign up for more classes than they actually plan to take.

Q: Does this course have recitations?

A: This term, we will have occasional recitations on Friday. They will be held at the same time and location as the Monday/Wednesday lectures. Some of them will review the material from the previous week. We might also include a few to review background and prerequisite material.

Whenever we are having a Friday session, it will be listed on the Schedule page.

Q: Where can I review the course policies?

A: See the Syllabus page for course policies.

Q: What does grading look like for this course?

A: The grading is based on exams, homeworks, and class participation. See more details in the Syllabus page.

Q: Does the course focus on real-world applications or theory of ML?

A: Both! As compared to 10-701, this course focuses a bit less on theory, but it certainly still makes a prominent appearance. See the machine learning course comparison for more details.

Q: Will there be programming language requirements for the homework?

A: Yes, you are required to use Python. You will be expected to know, or be able to quickly pick up, that programming language. Grading of the programming assignments will be done via Gradescope.

Q: Since 15-122 is one of the prerequisites, will I need to be proficient in C?

A: No, we will not require you to be proficient in C. See the programming language requirements question above.

Q: Do I have the appropriate background for this course?

A: Please see the Prerequisites section of the Syllabus page.

Also, check out our course comparison of the various Intro ML offerings. At the bottom of the course comparison is a self test. You can use it to gauge how comfortable you are with the appropriate math background. It might be appropriate for you to take MLD’s short course 10-606/607 that might help you catch up on any math background (10-606) or computer science background (10-607) that you are missing.

Q: As an undergrad, can I take this course if I haven’t met the official prerequisites?

A: In general, the only case I make exceptions for is the following: if you are missing only one prereq, will take it as a coreq, and can make a strong objective argument why you have the necessary background, then I will consider your case. If this applies to you, please email the instructor an unofficial transcript for a review of your prior coursework. In your email, please make a case for each prereq you’re missing. Most requests are denied.

Q: Is this course appropriate for someone interested in artificial intelligence?

A: Absolutely! Machine learning has become a key component of artificial intelligence systems deployed throughout the world. There are other excellent courses that provide a broader picture of AI as well (see 15-281, for example).