10-601BD, Fall 2018
School of Computer Science
Carnegie Mellon University
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 (Autolab, 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 and send a “Private Note” to the instructors that includes your Andrew ID.
A: Each semester, we run into the same problem: More students register for this course 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 “livestream” sections (Section D) this semester that are identical to the other sections 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 D will eventually get a seat? No. However, the historical stats are in your favor. Last spring, there was space for most students within several weeks. Last fall, about half the students got a seat.
So if you are currently waitlisted for Section B, we encourage you to sign up for Section D.
A: Click the video link on the Schedule page. Log in with your Andrew ID.
A: No one should be on the waitlist. Just sign up for either Section D (see above), both of which have infinite capacity. (There is a bug in the registration system that occasionally causes a waitlist on Sections D. However, someone will manually add you within a week.)
A: This term, we will have occasional recitations on Friday 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. We’ll interchangeably refer to these as “recitations” and “review sessions”.
Whenever we are having a Friday session, we will announce it ahead of time.
A: The final exam will be scheduled by the registrar sometime during the official final exams period. Please plan your travel accordingly as we will not be able accommodate individual travel needs (e.g. by offering the exam early).
A: See the About page for tentative course policies.
A: The grading is based on exams, homeworks, and class participation. See more details in the About page.
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.
A: Yes. Grading of the programming assignments will be done via Autolab. For each one, we will allow you to pick between a small predefined set of programming languages (last time there were two – we’ll at least allow Python). You will be expected to know, or be able to quickly pick up, that programming language.
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.
A: Please see the Prerequisites section of the About 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 new 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.
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.
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).