Information about INFO251 enrollment

Hello, You are most likely reading this because you are interested in enrolling in INFO251, "Applied Machine Learning." I created this page because I have received an overwhelming number of emails asking very similar questions. I'm hoping that this page can help answer your questions. Some key things to note:
  • This course has two prerequisites (see below). Students who do not satisfy these prerequisites will not be permitted to take the course. Note that the enrollment system does not perfectly enforce these prerequisites, but I do. Students who do not meet the prerequisites will be dropped from the roster after the first week of class!
    • INFO206 (or equivalent college-level course in computer science in Python). Other Berkeley courses that satisfy this requirement: CS61B
    • INFO271B (or equivalent graduate-level coursework in statistics or econometrics). Other Berkeley courses that satisfy this requirement: ARE213; ECON244; STAT215A.
  • If you haven't taken INFO206 and INFO271B (or one of the above alternatives), but believe you have satisfied the prerequisite through an alternatative course, please send me the following during the first week of classes: (i) a copy of your transcript that shows the grade you received in the class (S/U and auditing does not satisfy the requirment; (ii) a copy of the syllabus for the course in your transcript that covers equivalent material. I will not review these requests until the first week of class, so please don't send this information before then.
  • Students are expected to be proficient in Python prior to enrolling in INFO251 (this is one of the many skills students learn in INFO206). No lecture time or office hours will be spent teaching Python. If you don't know Python, learn it first and then take this course! Grades are based primarily on problem sets, and problem sets are based entirely on Python. Students who expect to pick up Python "on the fly" generally do not do well in this course.
  • Everyone is required to take the class for a grade - no S/U enrollment is permitted. Likewise, no auditing is permitted. These policies are designed to ensure that the students most committed to the class can have a seat in the room.
  • Priority enrollment is given to students from the School of Information. However, each year a handful of seats are reserved for studentsd from other units on campus. If you can't enroll directly, but are interested in taking the course, add yourself to the waitlist. While I strive to achieve some balance across units, when there are many students from the same unit, priority will be given to those higher up the waitlist.
  • Students who do not show up for the first two classes will be dropped from the roster.
  • There are many other machine learning courses at UC Berkeley, so if you don't get into INFO251 (or even if you do!), you might see if one of these is a better fit. The other options of which I am aware are: INFO254, CS189/289, DATA8, IEOR265, CS294, and CS281.