10707 (Spring 2019): Deep Learning

Temtative Dates:

Check Piazza for updates:

Assigment 1: Out: Jan 28th -- Due Feb 11th
Assigment 2: Out: Feb 18th -- Due March 4th
Assigment 3: Out: Mar 11th -- Due March 25th

Project Timeline

March 11: 3-page proposal on the class project
April 22nd, final projects are due, 8-pages

Assignments and grading

The course grade is a weighted average of 3 assignments (60%) and a final project (40%). Please write all assignments in LaTeX using the NIPS style file
Slightly modified NIPS style file and example atex (sty) (tex)

Homework Assignments

The assignments are to be done by each student individually. You may discuss the general idea of the questions with anyone you like, but your discussion may not include the specific answers to any of the problems.
Assigments will be submitted through Gradescope. Additionally, you should upload your code to Autolab. Writeups should be typeset in Latex and should be submitted in pdf form. All code should be submitted with a README file with instructions on how to execute your code.

Projects

For projects, you may work in teams of 2-3 people. Project info sheet pdf.

Slightly modified NIPS style file and example paper for latex (sty) (tex)
and formatting guide (pdf)
Please note that 8 pages is a hard upper limit on length. Do not go over.

Grace Day/Late Homework Policy

Homeworks: Each student will have a total of 5 grace days that a student may choose to apply to the homework assignments. No more than 3 grace days can be used on any single assignment. NOTE: Any assignment submitted more than 3 days past the deadline will get zero credit.

Projects: Each team will be given a total of 3 grace days on the project (can be split between project proposals and final reports). Unused grace days from homeworks CANNOT be applied to the project. NOTE: Projects submitted more than 3 days past the deadline will get zero credit.

Student Medical

Certificate or a written (not email) request submitted at least one week before the due date and approved by the instructor. Please plan ahead.

Marking

As a general rule, small matters of marking on assignments (apparent errors, questions about evaluation criteria, etc.) should be taken first to the marker (via email). More significant issues, or unresolved matters on assignments, are appropriate to take to the professor. Matters of marking on tests and exams should be taken to the professor.

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10707 (Spring 2019): Topics in Deep Learning || http://www.cs.cmu.edu/~rsalakhu/10707/