Deep Learning, Spring 2015
Syllabus (aka required reading)
Instructor: Yann LeCun - yann [ at ] cs.nyu.edu
Prerequisites: General machine learning course such as DS-GA-1003. Highly recommended: General understanding of Unix and proficiency in at least one programming language.
Teams: List of team names, Kaggle names and performance (ranking) across assignments. Teams need to communicate their setup within the first week with an email of the following content.
Version control: Github
- A group name must be chosen and a group 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 groups must be communicated to the TA via email and can only happen within the week an assignment is due.
- 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:
- 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.