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.
- Students who would like to use their laptops during the course are strongly encouraged to sit at the back-most row of the classroom.
- Please come to all lectures on time and leave on time, again so
that there are no distractions to the classmates.
PRE-REQUISITES
This course does not assume any prior exposure to machine learning theory or practice.
Students are expected to have the following background:
• A basic understanding of probability and statistics
• A working knowledge of linear algebra
• Basic programming skills, including familiarity with Python
• Experience using NumPy, pandas, and matplotlib for data manipulation and visualization
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 Canvas.
Students should submit their homework solutions (a pdf file with answers to conceptual questions and a Jupyter notebook with answers to programming questions) only electronically via Gradescope (no print outs).
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 class notes, slides, and the supplementary books mentioned on the
resources 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.
Questions and Re-grade Requests
- You should use Piazza for all your questions about the assignments and the course material. Instructor and TA(s) will do their best to answer your questions timely.
- Regrade requests should be done in writing/email,
- within 2 days after graded
assignments are distributed
- to the grader students specified on the front page (see Graders under People), and specifying
- the question under dispute (e.g., 'HW1-Q.2.b')
- the extra points requested (e.g., '2 points out of 5')
- and the justification (e.g., 'I forgot to divide by variance, but the rest of my answer was correct')
- In the remote case there is no satisfactory resolution, please contact the instructor.
Homework Grading and Solutions
- All homework will be graded online through Gradescope. Graders will provide comments and feedback on the deductions they have made accordingly.
- We will post solutions to the assignments on Canvas, 4 days after the due date (to account for students using slip days, see below).
Late Submission Policy
- No delay penalties, for medical/family/etc. emergencies (bring written documentation, like doctor's note).
- Each
student is granted '3 slip days' total for the whole course duration, to
accommodate for coinciding deadlines/interviews/etc.
That is, no questions asked, if the total delay is 4 days or less.
- You can use the
extension on any assignment during the course (unless otherwise stated). For
instance, you can hand in one assignment 3 days late, or
3 different assignments 1 day late each.
- Late days are rounded up to the nearest integer. For example, a
submission that is 4 hours late will count as 1 day late.
- After you have used up your slip days, any assignment handed in
late will be marked off 25%
per day of delay.
- To use slip days:
- upload your homework solutions on Gradescope to mark the time of submission
- You can upload your modified files multiple times at different points in time. However, please note that we will use your latest upload date as the date of submission, even if you have modified only a small part of your files.
Collaboration Policy
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.
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.
Policy on Use of Generative AI Tools
We encourage students to explore the use of generative
artificial intelligence (AI) tools, such as ChatGPT/Gemini/DeepSeek/etc., for all individual assignments. Any such
use must be appropriately acknowledged and cited, following the guidelines established
by the APA Style Guide, including the specific version of the tool used, along with the exact prompt used to generate the content as well as the AI's full response
in an Appendix. Because AI generated content is not necessarily accurate or appropriate, it is
each student's responsibility to assess the validity and applicability of any generative AI
output that is submitted. You may not earn full credit if inaccurate, invalid, or inappropriate
information is found in your work. Deviations from these guidelines will be considered
violations of CMU's academic integrity policy.
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 '95897' in the subject line of your email.
AUDITING
Auditing is not allowed. Only those students who are officially enrolled to take the course for credit are allowed to sit in class.
STUDENT WELLNESS
As a student, you may experience a range of challenges that can
interfere with learning, such as strained relationships, increased anxiety, substance use,
feeling down, difficulty concentrating and/or lack of motivation. These mental health
concerns or stressful events may diminish your academic performance and/or reduce your
ability to participate in daily activities.
You can learn more about confidential mental health services available on CMU campus at
here. Support is available 24/7 from Counseling and
Psychological Services: 412-268-2922.
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