Introduction

About the lecture

In this first mini-lecture we start by introducing machine learning. Where do we come across machine learning in our everyday lives, and where has it been seen in the history of science? Most shopping portals online employ machine learning practices to learn better what their customers might buy, and also use it to suggest similar products to customers to encourage further purchases. Through this we begin to understand how machine learning models can be trained: how we teach them to understand what we want to see as outputs. We then move onto the inspirations of machine learning and neural networks, the human brain. What similarities do they share? To finish off, we learn about how machine learning advanced the field of protein folding hugely in 2020, and the exciting prospects of this for future research. Finally we look at an example of a machine learning model: the kernel method.

About the lecturer

Mark Tuckerman obtained his B.S. in physics from the University of California at Berkeley in 1986 and his Ph.D. from Columbia University in 1993, working in the group of Bruce J. Berne. From 1993-1994, he held an IBM postdoctoral fellowship at the IBM Forschungslaboratorium in Rüschlikon, Switzerland in the computational physics group of Michele Parrinello. From 1995-1996, he held an NSF postdoctoral fellowship in Advanced Scientific Computing at the University of Pennsylvania in the group of Michael L. Klein. He is currently Professor of Chemistry and Mathematics at New York University. His research interests include the use of theoretical and computational chemistry techniques to study proton and hydroxide transport in bulk and confined hydrogen-bonded liquids and hydrogen diffusion in nanoconfined materials, the development of large time-step molecular dynamics and machine learning algorithms and free-energy based enhanced sampling tools for predicting the conformational equilibria of complex molecules and the exploration of structure and polymorphism in molecular crystals, and development of machine learning models in density functional theory and statistical mechanics. Honors and awards include the Japan Society for the Promotion of Science Fellowship, the Friedrich Wilhelm Bessel Research Award from the Alexander von Humboldt Foundation, the Camille Dreyfus Teacher-Scholar Award, an NSF CAREER Award, and the NYU Golden Dozen Teaching Excellence Award, the Sentinels of Science Award from Publons, the Andreas C. Albrecht Lectureship from Cornell University, and the Institute Lectureship from the Indian Institute of Technology, Kanpur.