This four-lecture seminar series introduces the basic concept of machine learning and its applications. The following topics will be covered: a) machine learning approaches (supervised learning, unsupervised learning, and reinforcement learning); b) deep neural networks (from biological neurons to artificial neurons, neural networks for supervised learning, and neural network training via empirical risk minimization); c) more advanced topics on neural networks (universal approximation theorem, stochastic gradient descent with backpropagation, and exploding and vanishing issues); and d) recent personal research on scientific deep learning approaches for learning forward and inverse solutions of PDEs.
Tan Bui-Thanh is an Associate Professor, and the endowed William J Murray Jr. Fellow in Engineering No. 4, of the Oden Institute for Computational Engineering & Sciences, and the Department of Aerospace Engineering & Engineering Mechanics at the University of Texas at Austin. Bui-Thanh obtained his PhD from the Massachusetts Institute of Technology in 2007, Master of Sciences from the Singapore MIT-Alliance in 2003, and Bachelor of Engineering from the Ho Chi Minh City University of Technology (DHBK) in 2001.
He has decades of experience and expertise on multidisciplinary research across the boundaries of different branches of computational science, engineering, and mathematics. Bui-Thanh is currently a Co-director of the Center for Scientific Machine Learning at the Oden Institute. He is a former elected vice president of the SIAM Texas-Louisiana Section, and the former elected secretary of the SIAM SIAG/CSE. Bui-Thanh was an NSF (OAC/DMS) early CAREER recipient, the Oden Institute distinguished research award, and a two-time winner of the Moncrief Faculty Challenging Award.
A/Prof. Tan Bui-Thanh
Oden Institute for Computational Engineering and Sciences
University of Texas at Austin
1 June 2023, 2-4pm: Lectures 1 and 2
5 June 2023, 2-4pm: Lectures 3 and 4
Online zoom link and
Anita B. Lawrence Room 4082 (on zoom).
This talk provides an introduction to the field of machine learning, in particular: