Foundations of Machine Learning for Materials Science . MATRL 289MT

Covers fundamental machine learning tools for materials science, including surrogate models for atomistic simulations, 3D image analysis, and experimental data analysis. It begins with probability theory and Bayesian inference, followed by regularized regression, Gaussian processes, and neural networks. Concepts of invariance and equivariance are introduced, with equivariant neural network architectures discussed. The course also covers ML methods for image analysis (e.g., variational autoencoders, diffusion models, GANs) and natural language processing.

You Might Also Like