연세대학교 계산과학공학

Short course on scientific deep learning / by Prof. Tan Bui-Thanh (2023.07.31~2023.08.10)

Short course on scientific deep learning

Tan Bui-Thanh
Associate Professor
Endowed William J. Murray, Jr. Fellow in Engineering No. 4
Co-Director of the Center for Scientific Machine Learning at the Oden Institute
The university of Texas at Austin

Time: 10AM to noon
Day:  7/31, 8/2, 8/4, 8/7, 8/9, 8/10
Place: ASTC 615, Yonsei University

This 12-hour short course on scientific deep learning provides an applied math perspective of deep learning. In particular, we shall employ elementary mathematics to understand, analyze, and provide insights into various aspects of deep learning including: the universal approximation capability, backpropagation, gradient vanishing, bias-variance trade-off, etc. We also provide a short introduction to Jax, an efficient and light-weight deep learning platform

The content of the short course:
Overview of Machine learning
Deep neural networks
Proof of Universal approximation theorem for ReLU neural networks
What could be the best neural network for a given task? Review/introduction to optimization
Computing optimal solution with gradient descent: derivation and convergence analysis
gradient descent for training neural network with backpropagation
gradient vanishing/exploding issues
Statistical learning for neural network
Motivation/introduction to probability theory
Statistical learning and the bias-variance decomposition/trade-off
Stochastic gradient and it its convergence analysis
Why deep could be better than shallow? (If time permits)
Introduction to deep learning in Jax

- Organizer: Eun-Jae Park (ejpark@yonsei.ac.kr)