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Deep Learning, Spring 2016


Syllabus (aka required reading)

Lecture: Tuesday, 7:10-9:00pm, in SILV 207
Tutorial: Thursday 4:55-5:45pm in SILV 520

Instructor: Zaid Harchaoui - zaid.harchaoui AaT nyu DoTt edu
Teaching Assistant: Junbo (Jake) Zhao - j.zhao [ at ] nyu.edu; Mayank Singh - ms8599 [ at ] nyu.edu; Yijun Xiao - yx887 [ at ] nyu.edu;
Please only send emails about personal issues.
Please prefix all your emails with the following tag:

- Linear Algebra
- Machine Learning (course such as DS-GA-1003)
- Unix/Linux basic commands and text editors such as Vim/Emacs
- Proficiency in at least one programming language

Recommended prerequisites:
- Algorithms
- Multivariable Calculus
- Probabilistic Graphical Model


Teams: Every assignment should be done in a group of two. Larger teams are not allowed.

Assignment submission: We'll use github repository as collaboration hub for assignment submission.
Jake's group; Mayank's group; Yijun's group


Video lectures

10% - A1
20% - A2
15% - A3
15% - A4
40% - Final Exam - May 18

Assignments are due at the beginning of the lecture. An assignment is graded based on Kaggle performance (competition), write-up and code.

Version control: Github

HPC Guide - please do make use of this cluster, if you want. GPUs will significantly speed up your code. Don't forget to read this guide on how to use CUDA with torch.

Torch + Utilities

Lateness policy:
Homeworks are due at 7:10pm (Lecture) or 4:55pm (Tutorial) sharp. Lateness penalties are: 1-10 mins = 5% of total points; 11-30 mins = 7% of total points; 31 mins - 24 hours = 10% of total points; longer = (10% of total points) x (number of days late, rounded UP). The Kaggle competition closes at the same time and there are no late submissions. Notice, that times on Kaggle are in UTC. We are in EST.

- Every assignment should be done in a team of two. Larger teams are not allowed.

- A team name must be chosen and a team leader must communicate her/his Kaggle name to the TA within the first week (registered on an nyu email address). The link to the Kaggle competition will be posted with the assignment. The required detail per write-up may vary across assignment and will be specified as well. Movements between teams must be communicated to the TA via email and can only happen within the week an assignment is due.

- Each TA will be in charge of a group of teams. One needs to make sure he/she is reaching out to the right TA before submit question via Email or Piazza.

- Exams will be closed book, closed notes.

- The teaching staff will use piazza to send out announcements. Please check it regularly.

- In order to pass the course, you need to get at least 30% of the possible points on the final exam. The final exam is designed to test your knowledge of the assignments and then ask other theoretical questions.

On academic integrity:
We expect you to try solving each assignment within your own team. However, when being stuck on a problem, I encourage you to collaborate with other students in the class, subject to the following rules:

- You may discuss a problem with any student in this class, and work together on solving it. This can involve brainstorming and verbally discussing the problem, going together through possible solutions, but should not involve one student telling another a complete solution.

- Once you solve the homework, you must write up your solutions within your own team, without looking at other people's write-ups or giving your write-up to others.

- In your solution for each problem, you must write down the team names of any team with whom you discussed it. This will not affect your grade.

Please also follow the GSAS regulations on academic integrity found here http://gsas.nyu.edu/page/academic.integrity

Last year's website: http://cilvr.nyu.edu/doku.php?id=courses:deeplearning2014:start - Good source of links to papers.

All product names used in this website (www.kaggle.com, www.piazza.com) are trademarks of their respective owners, which are in no way associated or affiliated with NYU.

/srv/www/cilvr/htdocs/data/pages/courses/deeplearning2016/start.txt ยท Last modified: 2016/01/28 20:51 by jake
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