CS 519-006 Deep Learning

School of Electrical Engineering and Computer Science
Oregon State University
Corvallis, OR 97331

Instructor: Fuxin Li (KEC 2077)
Class Time: TR 4-5:20pm, Location: KEC 1001

TA: Mingbo Ma, Xu Xu

Office Hours:
TA: Mingbo Ma, Friday 4-5PM, KEC 4125 (4th floor lounge)
Xu Xu, Tuesday 10-11AM, KEC 4125 (4th floor lounge)
Fuxin Li: Monday 3-4PM, Wednesday 10-11AM, KEC 2077.

[registrar entry]

We use Canvas for discussions and grades. For technical questions (e.g., HW), first check if the same question has been asked on Canvas; if not, ask it there (we monitor the discussions). You will be rewarded for answering questions on Canvas. For non-technical questions (e.g., grading), .

Schedule/Slides/HWs


In a Nutshell

An introduction to the concepts and algorithms in deep learning; basic feedforward neural networks, convolutional neural networks, recurrent neural networks, deep belief nets, autoencoders and deep networks in reinforcement learning.

Prerequisites: CS 534 Machine Learning or equivalent knowledge recommended.


Textbooks:

Recommended textbook (recommended; this course is self-contained):

Learning Objectives of the Course:

  1. Have a solid understanding in the concepts of deep learning
  2. Gain some intuitions on deep networks, understand why do they perform well in practice
  3. Understand the training of deep learning models and able to explain and toggle parameters
  4. Be able to use at least one deep learning toolbox to design and train a deep network
  5. Be able to design new deep learning algorithms and architectures.

Assessment

Assignments

Programming assignments can be performed in MATLAB or Python. At least one assignment would be a written assignment about the essential mathematical foundations.

Final Project

A self-designed project on deep learning on your specific data source. Some default project topics will be provided.

NO FINAL EXAM.

Important Note: Please note that all homework (except the final project) should be your own work. Any collaboration that requires written communication is forbidden. You should also not copy answers from books or internet resources.


Collaborations:

You are encouraged to study together and discuss general strategies for solving problems but not at the level of written solutions. Please read the department's academic dishonesty policy for more details. You should not use any web sources for answering the homework questions unless explicitly instructed to do so.

Fuxin Li