CS6886: Systems Engineering for Deep Learning 

Many thanks to the following awesome people for making the course possible!

"I hear and I forget. I see and I remember. I do and I understand."

 Confucius

Introduction

This course is a practical and project based introduction to end-to-end systems design challenges in Deep Learning from the perspective of Computer Architecture. It will cover topics from three fields: Machine Learning, Compilers and Architecture. It targets self-motivated graduate students and upper level undergraduates  interested in pursuing the area in greater depth, potentially for a research project or thesis. It is NOT a traditional course where textbook topics are covered during lectures and exams evaluate the student's understanding of the material. It is a course with in-class tutorials, hands-on assignments and a significant course project. Hence,  students are expected to do original work that can potentially be extended even beyond the course itself. 

Evaluation

Students will be evaluated on the following basis:

• Course Project = 40%  of the grade

• Five Assignments, 10% each = 50% of the grade

• Five paper reading reports, 2% each = 10% of the grade

Time Table and Location

The course will be taught on the F slot, with the timings below in the Aryabhatta Hall (CS25) of the CSE Department :

• Tuesdays 17:00-17:50

• Wednesdays 11:00-11:50

• Thursdays 09:00-09:50

• Fridays 08:00-08:50

Twenty students worked hard through the semester and completed the course successfully. Kudos to every one of them! A significant part of their time was spent working on a project of their interest. Details about these projects are provided below: