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Course Policies
LECTURES
- All devices such as laptops, cell phones, noisy PDAs, etc.
should be turned off for the duration of the lectures and the
recitations, because they may distract other fellow students.
- Please come to all lectures on time and leave on time, again so
that there are no distractions to the classmates.
PREREQUISITES
Students are expected to have the following background:
- Basic knowledge of probability and statistics
- Basic knowledge of linear algebra and algorithms
- Working knowledge of basic computing principles
- Basic programming skills at a level
sufficient to write a reasonably non-trivial computer program in a
language of preference
ASSIGNMENTS
Assignments are due at the * beginning of lecture * on the due date.
The due date of assignments are posted at the assignments page.
Assignments will be posted on Blackboard.
Students should submit the programming part of their assignment electronically via Blackboard.
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-level 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
front page 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.
Academic integrity
All students are expected to comply with CMU's policy on academic integrity. Please read the policy and make sure you have a complete understanding of it.
Collaboration
You are encouraged to discuss homework problems with your fellow students. However, the work you submit must be your own. You must acknowledge in your submission any help received on your assignments. That is, you must include a comment in your homework submission that clearly states the name of the student, book, or online reference from which you received assistance.
Submissions that fail to properly acknowledge any help from other students or non-class sources will receive NO credit. Copied work will receive NO credit. Any and all violations will be reported to the Heinz College administration and may appear in the student's transcript.
Questions and requests
- All questions should go on to the Piazza system. Instructor and TA(s) will do their best to answer your questions timely.
- 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.
Late policy
- 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 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.
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- Late days are rounded up to the nearest integer. For example, a
submission that is 4 hours late will count as 1 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 your slip days are used up, 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.
EMAIL
Piazza should be used for general course and assignment related questions. For other types of questions (e.g., to report illness, request various permissions) please contact the instructor directly via email.
Please make sure to include '95828' in the subject line of your email.
AUDITING
- 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 give a final presentation with
everyone. You don't need to submit a milestone or final paper. You must
get at least 80% on the presentation part of the project.
- Please, email the instructor 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.
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Last modified by Leman Akoglu, Oct 2016
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