IST 597:  Foundations of Deep Learning
  Fall 2017

Instructor: Dr. C. Lee Giles

TA: Alex Ororbia

Time and Place: Fall, 2017. 2:30-5:30, Wednesday, 108 Sackett Bldg.

Office Hours:  Giles, Thursday 3-4 E350 Westgate Bldg.
                         Ororbia, Monday and Friday 2:30-3:30 E302 Westgate Bldg.

Course Overview

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.

Course Mission Statement:

This course is intended to prepare students to understand, design, develop and use deep learning methods.

Course Prerequisites:

Students should be able to learn the basics of deep learning fundamentals.

Schedule (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 material.

Course Materials and References:
Course materials can be found here. There will also be links on the schedule.

        Project 70%
                        Exercises 30%

Texts and Readings: 

The reading list is on the schedule above.  We will use chapters and sections from the online text:

Email: 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.