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Stony Brook University
Machine Learning
CSE512 - Spring 2014


Course Policies


  • All devices such as laptops, cell phones, noisy PDAs, etc. should be turned off for the duration of the lecture is and the recitations, because they may distract our fellow students.
  • Please come to all lectures on time and leave on time, again so that there are no distractions to the classmates.


Students are expected to have the following background:
  • Working knowledge of probability theory and statistics.
  • Working knowledge of linear algebra and algorithms.
  • Working knowledge of basic computer science principles at a level sufficient to write a reasonably non-trivial computer program in a language of preference.


Important Note: As we reuse problem set questions, covered by papers and webpages, we expect the students not to copy, refer to, or look at the solutions in preparing their answers. Since this is a graduate class, we expect students to want to learn and not google for answers. The purpose of problem sets in this class is to help you think about the material, not just give us the right answers. Therefore, please restrict attention to the books mentioned on the webpage when solving problems on the problem set. If you do happen to use other material, it must be acknowledged clearly with a citation on the submitted solution.

Questions and requests

  • All questions should go to the TA(s), the instrcutor, or on the 'blackboard' system.
  • Regrading requests should be done in writing, via e-mail to the TAs and the instructor, at the latest 48 hours after graded exams or assignments are distributed by the instructor or the TAs.

Late policy 

  • The due date and time of assignments are posted on the course web page.
  • Slip days: To accommodate for coinciding deadlines you may have from other courses, or personal unforeseen events such as sickness, each student is granted an automatic extension of 4 calendar days. You can use the extension on any assignment(s) remaining during the semester. For instance, you can hand in one assignment 4 days late, or each of four assignments 1 day late.
    • Late days are rounded up to the nearest integer. For example, a submission that is 4 hours late will count as one day late.
    • When you hand in a late assignment, you must identify at the top of the assignment, (i) how late this assignment is, and (ii) how much of the total slip time you have left.
    • After you have used up your slip time, any assignment handed in late will be marked off 25% per day.
  • Additional, no-penalty extensions will be granted only in extreme situations (medical emergency, immediate family emergency). Contact the instructor, with written documentation, like doctor's note.


  • If you are a student, and you don't want to take the class for credit, you must register to audit the class. To satisfy the auditing requirement, you must either:
    • Do *three* homeworks, and get at least 75% of the points in each; OR

    • Do a class project and do *one* homework, and get at least 75% of the points in the homework.
        Like any class project, it must address a topic related to machine learning and you must have started the project while taking this class (can't be something you did last semester). You will need to submit a project proposal with everyone else, and present a poster with everyone. You don't need to submit a milestone or final paper. You must get at least 80% on the poster presentation part of the project.

    • Please, send us an email saying that you will be auditing the class and what you plan to do.

  • If you are not a student and want to sit in the class, please get authorization from the instructor.

Last modified: 2014, by Leman Akoglu