Bayesian Machine Learning

Speaker:

Prof. Nicholas Zabaras

Time:

July 3 – July 5, 2019. 9:30am–11:30am

Inviter:

Prof. Qifeng Liao

Location:

SIST 1-A108

Objectives
The objective of this summer school at ShanghaiTech is to introduce selective Bayesian inference methods for unsupervised and supervised learning. This short course will cover introductory topics in Bayesian statistics with linear and logistic regression models used for demonstrating key concepts. As time allows, additional topics may include mixture models and the EM algorithm, Latent Variable Models, and Variational Methods. The course is appropriate for graduate students in Engineering and the Sciences, Mathematics/Statistics and Computer Science.

Prerequisites
The course is for graduate students in Engineering and the Sciences. While basic aspects of Bayesian statistics will be reviewed earlier background in computational statistics is desirable.

 Course Schedule
This course will include 6 hours of lectures (two per day of instruction) plus office hours to accommodate student questions.

Syllabus

·Review of computational Bayesian statistics (priors, likelihoods, posteriors, inference, maximum likelihood and Bayesian learning).

·Stochastic Optimization, Iteratively reweighted least squares (IRLS), Bayesian logistic regression, Laplace approximation, Online learning, the LMS algorithm.

·Generalized Linear Models and the Exponential Family, MLE and MAP estimation, Bayesian inference.

If time allows additional topics will be highlighted including:

·Mixture Models and the EM algorithm

·Latent Variable Models

·Variational Inference

Textbooks
Lecture notes and references will be provided. The following books are also recommended:

·C Bishop, Pattern Recognition and Machine Learning, 2007

·K. Murphy, Machine Learning: A probabilistic perspective, 2012.

Bio:

· Prof. Nicholas Zabaras joined Notre Dame in 2016 as the Viola D. Hank Professor of Computational Science and Engineering. He is the Director of the interdisciplinary University of Notre Dame “Center for Informatics and Computational Science (CICS)” that aims to bridge the areas of data-sciences, scientific computing and uncertainty quantification for complex multiscale/multiphysics problems in science and engineering. Among the various appointments, Prof. Zabaras was until recently the Hans Fisher Senior Fellow with the Institute for Advanced Study at the Technical University of Munich where he currently holds the position of TUM Ambassador. He served for nearly 23 years at all academic ranks on the faculty of Cornell University.