Pierre Baldi

[intermediate/advanced] Deep Learning: Applications in the Natural Sciences

Summary

First these lectures will provide an introduction to the history and theory behind deep learning. Then the focus will shift to the many applications of deep learning to the natural sciences, by demonstrating how deep learning methods can be used to tackle beautiful and fundamental problems in physics, chemistry, and biomedicine. Examples of problems that will be considered include the search for exotic particles and dark matter, the prediction of molecular properties and chemical reactions, and the classification and understanding of images and videos in biomedical imaging. Some of the likely future trends and related ethical and societal issues will be discussed at the end.

Syllabus

1. Historical Introduction. Theory of Deep Learning.

2. Applications in Physics and Chemistry.

3. Applications in the BioMedical Sciences.

4. Future trends and technical, ethical, economic, and societal frontiers.

References

P. Baldi. Deep Learning in Science. Cambridge University Press, 2021

Pre-requisites

Basic calculus, algebra, and probability theory. Some familiarity with machine learning and neural networks.

Short bio

Pierre Baldi earned MS degrees in Mathematics and Psychology from the University of Paris, and a PhD in Mathematics from the California Institute of Technology. He is currently Distinguished Professor in the Department of Computer Science, Director of the Institute for Genomics and Bioinformatics, and Associate Director of the Center for Machine Learning and Intelligent Systems at the University of California Irvine. The long term focus of his research is on understanding intelligence in brains and machines. He has made several contributions to the theory of deep learning, and developed and applied deep learning methods for problems in the natural sciences in physics, chemistry, and biology. He has written five books and over 300 peer-reviewed articles. His honors include the 1993 Lew Allen Award at JPL, the 2010 E. R. Caianiello Prize for research in machine learning, and becoming a Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB.