CS089/CS189 | Advanced Topics in Deep Learning

Course description

This is an advanced research topic course for students that have already taken COSC 78/178, Deep Learning. This class is designed to help students develop a better understanding of the state of the art in deep learning. Students will explore new research directions and applications of deep learning by reading, presenting and discussing recently published papers in this area. In addition, students will be required to propose and complete a term project in the area of deep learning.

Administrative information

Instructor: Lorenzo Torresani | 109 Sudikoff | office hour: by appointment
Lectures: Tue&Th 10:10am-12pm | x-hour (used occasionally to make up cancelled classes) W 3:30pm-4:20pm
Location: Sudikoff 213

Grading and policies

Grading scheme

The final course grade will be based 20% on in-class participation, 20% on the written critiques, 20% on the paper presentations, and 40% on the term project.

Prerequisites

Computer Science 78/178 (Deep Learning).

Project Schedule

Project pre-approval: 4/14/2017.

Project proposal (write-up + presentation): 4/18/2017.

Project milestone (write-up + presentation): 5/11/2017.

Project final (write-up + presentation): 5/30/2017.

Late submissions

Late submissions will not be accepted under any circumstances: you will get a zero grade for any late submission.

No-laptop policy

We have a no-laptop policy in class (texting, sleeping or engaging in other activities unrelated to the lecture is also forbidden). This policy will be strictly enforced so as to encourage active participation by all students and to avoid distracting people that are focusing on the lecture.

Auditing

Please contact the instructor if you would like to audit the course.

Academic integrity

In order to encourage independent critiquing and to foster in-class discussion, you will NOT be permitted to talk about the assigned papers with your classmates before the lecture: I am interested in hearing your own personal views of the articles and not consensus opinions emerged from group discussions held before the lecture.

You are allowed to use external software for portions of your project. However, you should clearly report the use of external code and include pointers to such software in your project write-up. The project grade will be based on the novelty of your solution/application but also on the amount of new code written by you to implement the idea. So keep this in mind when considering to use software written by someone else.

These rules will be strictly enforced and any violation will be treated seriously