The artisan court at brickyard
Venue: BYAC 110
Time: Friday: 9:40AM – 12:10PM
Session Closed
Instructors: Baoxin Li and Ragav VenkatesanThis 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.
The syllabus of the course can be found here. A tentative and updated plan for the course is also available here. Course now has its own github repo. Any course-related code will be posted here.
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.
All the material posted in this webpage and on the course blackboard including lecture notes, lecture audios, homework, miniproject and project questions, and other materials are all intellectual property owned by the instrutors and other persons. You are not allowed to share, re-destribute, or host on public domain any of the material, either in part or full.