Introduction to Machine Learning

10-301 + 10-601, Fall 2021
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 10-301/601?

A: Undergraduates must register for 10-301 and graduate students must register for 10-301/601. 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 of the lectures. Everything else will be the same, including the instructor, course content, homeworks, exams, policies, etc.

Q: What are Sections C, D, and E?

Section D is for students who have university permission to enroll remotely in the course. Section C and Section E will not be used this semester.

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. In those cases, you may join via 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.

Only the 1:25 PM lecture/recitation will be livestreamed and recorded. The recitation recording will only be available for 24 hours.

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. The exact time and location depends on which section you are in (Sections A and B/D). Consult your course schedule for details. Some of them will review the material from the previous week. We might also include a few to review background and prerequisite material. We’ll interchangeably refer to these as “recitations” and “review sessions”.

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

Q: What is the difference between Sections A and B/D?

A: Sections A and B/D are the recitation sections. The only difference between them is the time and location. The content of the recitations will be the same. The recitations will be lead by a team of TAs responsible for that recitation’s topic, so you’ll get to know different TAs throughout the semester.

Q: Will the recitations be in-person or livestreamed?

A: All recitations sections (Sections A and B/D) are in-person, since they highly interactive problem solving sessions. That said, if you are for some reason unable to come in person, you may also watch the livestream of the 1:25 PM recitation on Fridays; the other recitations are not livestreamed.

Note that video recordings of the recitation will only be available for 24 hours.

Q: Where can I review the course policies?

A: See the Syllabus page for tentative 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. Grading of the programming assignments will be done via Gradescope. For each one, we will allow you to pick between a small predefined set of programming languages (last time there were four: Python, C++, Java). You will be expected to know, or be able to quickly pick up, that programming language.

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. Though there is a (very small) chance it would be one of the supported programming languages. 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 15-381 and 15-780 for example).

Q: I am a remote student in Section D, what should I expect?

A: Please see the “Remote Student Engagement” section of the Syllabus.