IST 597: Foundations of Deep Learning
and Place: 2:30-5:30, Wednesday, 121 Earth and
Hours: Giles, Thursday 3-4, E350 Westgate Bldg.
Mali, Tuesday 2:30-4, Thursday 10:30-12, E345 Westgate Bldg.
Kaixuan Zhang, Monday 2:30-4, Thursday 4-5:30, E301 Westgate Bldg.
This is a three hour course hands on course for graduate students
that meets once a week. The course will introduce the mathematical
foundations of deep learning: linear algebra, numerical computation,
and machine learning basics. We will then 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. 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 also there; most classes will have
online handouts such as powerpoint or PDF. 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 proposal and
Final project presentation and paper: 40%
information form: this must be filled out and turned in order to
receive a grade.
There will be 5 exercises, each counting 8 points, all in
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.
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.
- For TensorFlow, there are many books and online
tutorials. Some recommended books are:
Students will be placed in teams.
Each team will propose and investigate an interesting project on
deep learning. Machine learning competitions are possible projects
as are applications, comparisons and original research. For
suggestions, please see the instructor or the TA.
There will be a proposed topic presentation, a final presentation,
and a project paper in PDF and hard copy in the ACM
There will be two presentations for the project and two for assigned
research papers to review.
For the assigned papers, each team will make a review presentation
on their two assigned paper(s).
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).