Introduction to Machine Learning

10-601, Spring 2017
School of Computer Science
Carnegie Mellon University

Frequently Asked Questions

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, the waitlist for 10-601 often clears several 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.

Q: When is the final exam?

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).

Q: Where can I review the course policies?

A: See the About page for tentative course policies.

Q: Are there any differences between 10601A and 10601B?

A: For the Spring 2017 semester, 10-601A and 10-601B 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 does grading look like for this course?

A: The grading is based on exams, homeworks, and class participation. See more details in the About 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: Where can I view the syllabus?

A: In the past, the syllabi for 10-601A and 10-601B had different foci. This semester, we hope to combine the best aspects of those previous versions of the course into one. At the moment, the syllabus isn’t finalized, but you can look at the Fall 2016 syllabus for 10-601A and 10-601B to get a sense for what the Spring 2017 syllabus will look like.

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

A: Grading of the programming assignments will be done via Autolab. These assignments will require you use a specific programming language. You will be expected to know, or be able to quickly pick up, that programming language.

For each programming assignment, we will allow you to pick between Python and Octave (an open source version of Matlab).

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 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 10-600 (to be taken at the same time as 601/701) that might help you catch up on any math background. It will likely be offered again in Fall 2017.

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

A: We certainly do strongly prefer students to take the prerequisites ahead of time. We give priority to students that have met the prereqs. 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.

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: Does this course have recitations?

This term, we do not plan to have required weekly recitations. Instead, we plan to give several optional review sessions intended to prepare you for the homework and the midterm / final exams. We might also include a few to review background and prerequisite material. In total, we might have 8 - 10 such review sessions. We’ll interchangeably refer to these as “recitations” and “review sessions”.