MACHINE LEARNING IN CHEMISTRY

CHEM 635

An introduction to computational chemistry. Covers new techniques and developments in the literature, and specific types of calculations that are relevant to current research and needs.

PHYSICS 361

A detailed introduction to the use of machine learning techniques in physics. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. A selection of physics topics that are particularly amenable to analysis using machine learning will be discussed. These might include processing collider data, classifying astronomical images, solving the Ising model, parameter estimation from physics data sets, learning physical probability distributions, finding string theory compactifications, and finding symbolic physical laws.

CHEM 661

Basic chemical thermodynamics with applications to chemical and phase equilibria and the study of solutions; introduction to statistical mechanics and calculation of thermodynamic quantities from molecular models; stability and fluctuations.

MS&E 561

Introduction to applications of data science in materials science and engineering, including understanding the creation and use of modern data resources, data-centric approaches to materials, and the integration of machine learning across the materials landscape.

CBE 355

Concepts and techniques from statistics, data science, and machine learning applied to problems in chemical engineering. Random variables, estimation, statistical data analysis and learning, and decision-making under uncertainty.