IST 597: Foundations of Deep Learning
and Place: 2:30-5:30, Tuesday, Westgate W219
Hours: Giles, Westgate E350, 2:30-3:30 PM Wednesday
This is a three hour course hands on course for graduate students
that meets once a week. Students should be familiar with the
mathematical foundations of deep learning: linear algebra, numerical
computation, probability and information theory. There will be a
brief introduction to machine learning basics and computation
complexity. We will cover modern practical deep networks and their
applications. We will investigate deep feedforward networks,
convolutional networks, and recurrent networks as well as practical
methodologies, including model regularization and parameter
optimization. We will also cover transformers, adversarial, science
inspired, and large language models. Practical applications will be
utilized for course projects. Students will learn deep learning
software from the projects and will give presentations on chosen
Course Mission Statement:
This course is intended to prepare students to understand, design,
develop and use deep learning methods.
Students should be familiar with the basic mathematics of deep
Schedule (syllabus): This schedule is subject to change. Please check it
on a regular basis for assignments.
- The reading list and student team presentation order is on the
- Most classes will have online handouts such as powerpoint or
PDF and examples of related code. It is the student's
responsibility to download that material.
Materials and References: Course materials can
be found here. There will also be links on the schedule.
Project: 50% total.
Project proposal and
Final project presentation and paper: 40%
Paper reviews and
There will be 3 exercises, each varying in points, all in Python or
All exercises are due by midnight of the due date and must be
submitted as a zip or gzip which includes PDF and PythonScript[.py].
Late exercises will receive 50% credit for the first 24 hours and no
credit after that time. All exercise solutions must be turned
in, even if late.
Failure to do so can result in a deferred grade.
Texts and Readings:
The reading list is on the schedule above. We will use
chapters and sections from the online text Deep Learning
and related papers listed in the syllabus.
- Deep Learning,
Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press,
- Papers where appropriate.
- Other Deep Learning books
- Books that discuss some of the advantages and disadvantages
of Deep Learning are:
Student teams will have a research project, either proposed or
assigned by Giles and TA.
All proposed projects will have to be approved.
There will be a topic presentation, two updates, a final
presentation, and a project paper in PDF and hard copy in the ACM
There will be at two detailed presentations for the project and
For the assigned papers, each team will make a review
presentation on their assigned paper(s).
Each eam will write two questions for each presentation and ask
one (not required by the presenting team).
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. This course
Educational Resources (OER).