This course covers the fundamentals of deep neural networks. We introduce multi-layer perceptrons, back-propagation, and automatic differentiation. We will also discuss Convolutional Neural Networks, Recurrent Neural Networks, Graph Neural Networks, and Generative Adversarial Networks. In this course, you will learn the foundations of Deep Learning, understand how to build deep neural networks, and learn how to participate in machine learning competitions.
Lecturer: Qi (Rose) Yu (firstname.lastname@example.org)
TA: Rui (Ray) Wang (email@example.com), Mayank Sharan (firstname.lastname@example.org), Zheng Ding (email@example.com)
Lecture Time: 3:30 pm - 4:50 pm PT, Tuesday, Thursday
Discussion Time: 5 - 5:50 pm PT, Wednesday, Center Hall 214
Rose Yu | 4:00 pm - 4:50 pm | Monday |EBU3B 3208
Zheng Ding | 10:00 am - 10:50 am | Tuesday |EBU3B B250A
Mayank Sharan | 5:00 pm - 5:50 am | Thursday |EBU3B B260A
Rui Wang |10:00 am - 10:50 am | Friday |EBU3B B250A
Location: Waren West
Week 1 (Mar 28) Introduction and machine learning recap HW 1 release
Week 2 (Apr 4th) Multi-layer perceptron
Week 3 (Apr 11th) Convolutional neural network HW 2 release
Week 4 (Apr 18th) Recurrent neural network
Week 5 (Apr 25th) Deep learning implementation
Week 6 (May 2nd) Mid-term week
Week 7 (May 9th) Deep learning theory
Week 8 (May 16th) Graph neural network Milestone report due
Week 9 (May 23rd) Generative adversarial network
Week 10 (May 30 th) Presentation week Final report due
30 % homework (15% x 2)
25 % Mid-term exam
40 % Kaggle competition
5 % milestone report
15 % final report
10 % final presentation
10 % competition ranking
5 % class participation
Q: What are the pre-requisites?
(MATH 31BH or MATH 20C) and (ECON 120A or ECE 109 or CSE 103 or MATH 181A or MATH 183, MATH 170A);
Proficiency in Python.
Q: Can first year undergraduates take this course?
Restricted to students with sophomore, junior, or senior standing within the CS25, CS26, CS27, CS28, EC26, and DS25 majors.
All other students will be allowed as space permits.