This course will introduce some basic paradigms for deep learning, with a focus on deep architectures using convolutional neural networks. Topics to be covered include:
The course is intended to be interactive by requiring students to read the textbook or papers before each lecture, and the lecture time will be used to share insights, discuss pros and cons of any techniques to be studied in that lecture. There will be approximately 8 weeks of lectures by the instructors, and the rest will be on paper discussion and students will be required to make presentations. The students are required to understand all the papers presented, since there will be a final exam that will contain questions about the presented papers. Time permitting, the class may invite other faculty or senior PhD students who have relevant research work and recent publications on deep learning to give guest lectures.
There are no prescribed textbooks that is mandatorily required. The material for most of the course will come from the following book:
Being a graduate-level course on an up and coming topic, this course will also involve material from materials not published in books but in peer-reviewed conferences and journals.
Students should have good working knowledge of calculus, linear algebra and basic probability theory. It is highly recommended for a student to first take at least one machine learning class (e.g., CSE 569 or CSE 575 at ASU) before registering for this course. This will be a seminar course and will cover fairly advanced techniques and their applications without spending much time on reviewing the basics; a student who has not taken any graduate-level machine learning class will find it extremely difficult to succeed in this class. In addition, proficiency in programming in python is required for doing the course projects and/or mini project assignments.
© 2017 Ragav Venkatesan and Baoxin Li.
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