Schedule
Mondays 5:00 pm-7:30pm at Room 130, Sociology-Psychology Building, Duke University, beginning Monday, August 26, 2019
No class during week of Thanksgiving (November 25, 2019)
Last class: Monday, December 2, 2019
Instructor: This course will be taught by David Banks, Professor of the Practice of Statistics, Duke University & Director, SAMSI
Course Outline:
This course is being offered in conjunction with the SAMSI semester-long research program on Deep Learning. The course will start with a review of standard neural networks, and then progress to modern deep learning, including convolutional neural networks, recursive neural networks, generative adversarial networks, and various kinds of autoencoders. We shall discuss training strategies, architecture search, regularization and quantization.
There will be mathematics in the course, and a degree of mathematical sophistication is expected from the students, but the material will all be self-contained. The emphasis will be upon heuristics and applications. There will be projects and presentations at the end of the semester, and students will work on those in small groups. Each group will need to have at least one member who can program in Python or a comparable language.
Assignment Collaboration Folder
** Click HERE to post homework assignments or other deliverables assigned by instructor **
Registration:
- NCSU – MA 810.001 / ST 810 005
- UNC-CH – STOR 894.001 and MATH 892.001
- Duke STA 790.01 / MA 790-71.01
Monday, August 26, 2019
| Date | Topic | Speaker | Slides |
|---|---|---|---|
| August 26, 2019 | Deep Learning – Applications and Overview | David Banks | |
| Homework Assignment – Data Access | Word Doc | ||
| Team Sign Up Information | Word Doc | ||
| September 2, 2019 | Probability and Information Theory | David Banks | |
| September 9, 2019 | Numerical Computation | David Banks | |
| September 16, 2019 | Machine Learning Basics | ||
| September 23, 2019 | Deep Feed Forward Networks | ||
| September 30, 2019 | Regularization | ||
| Oct 7, 2019 | Fall Break – NO CLASS | ||
| October 14, 2019 | Optimization | ||
| October 21, 2019 | Convolutional Neural Networks | ||
| October 28, 2019 | Recurrent and Recursive Neural Networks | ||
| November 4, 2019 | Guest Lecture – Empirical Risk | ||
| November 11, 2019 | Autoencoders | ||
| November 18, 2019 | Generative Adversarial Networks | ||
| November 25, 2019 | Practical Issues/Tricks |