Math 680J Optimal Transport and Deep Learning
I am excited to have you in my class this Spring. Through this semester we will introduce basic tools from the optimal transport theory, deep neural networks and their training, as well as a selected set of research topics in deep learning.
Regarding to the pandemic we are in, we want to do everything we can to protect you, your classmates, your professors/TAs, and your family members -- everyone really-- from possible infection. This is why we ask that you follow the guidelines laid out by ISU at ISU Covid-19 Information be responsible for abiding by the university’s COVID-19 health and safety expectations.
Here below is the course info:
- Instructor: Hailiang Liu
Jan 25: course opens
Weekly: TR 11:00A -- 12:15P, Virtually via Zoom.
May 06: course ends
Course content and references:
The material will be based on lecture notes and a collection of book chapters.
Part I: Introduction to Optimal Transport
 F. Santambrogio.
Optimal transport for applied Mathematicians. Vol. 87, Progress in Nonlinear Differential Equations and Their Applications, Birkhaueser, 2015.
Part II: Introduction to Deep Learning
 Ian Goodfellow and Yoshua Bengio and Aaron Courville.
Deep Learning. MIT Press, 2016; http://www.deeplearningbook.org
Topics We plan to have 25 regular lectures. Plus one review and two project presentations.
Part I Introduction to optimal transport theory [10 lectures, week 1~6]
1. Primal and dual problems
2. Minimal flows and transport density
3. Wasserstein spaces
4. Numerical methods
5. Gradient flows
Part II: Introduction to Deep Learning [16 lectures, week 6~14]1. Mathematics and machine learning basics
2. Deep neural networks and training
3. Deep learning research
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