IST 597: Foundations of Deep Learning
and Place: Fall, 2017. 2:30-5:30, Wednesday, 108 Sackett
Hours: Giles, Thursday 3-4 E350 Westgate Bldg.
Ororbia, Monday and Friday 2:30-3:30 E302 Westgate Bldg.
This is a three hour course for graduate students that meets once a
week. The course will introduce the mathematical foundations of deep
learning: linear algebra, probability and information theory,
numerical computation and machine learning basics. We will then
cover modern practical deep networks and their applications. This
means we will cover deep feedforward networks, convolutional
networks, and recurrent networks as well as practical methodologies,
including model regularization and parameter optimization. Practical
applications will be utilized for course projects.
This course is intended to prepare students to understand, design,
develop and use deep learning methods.
Students should be able to learn the basics of deep learning
(syllabus): This schedule is
subject to change. Please check it on a regular basis for
assignments. The reading list is here; most classes will have online
handouts. It is the student's responsibility to download that
Materials and References: Course materials can
be found here. There will also be links on the schedule.
Grading: Project 70%
Texts and Readings:
The reading list is on the schedule above. We will use
chapters and sections from the online text:
- Deep Learning,
Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press,
- Papers where appropriate.
All email to the instructor and TA about this class should contain
"IST597" in the subject line. For example, the subject line
might read "IST597: Question about ....". Email without this
information might be deleted by spam filters or placed in a folder
to be read at a later date. Email with the appropriate
identifier will usually be read within 24 hours of being received.
Reuse: Materials from this
course can be publicly reused in other courses.