Machine Learning for Chemistry

Introduction to machine learning and its many applications within chemical sciences. Topics include widely-used approaches for modeling large and complex data sets, including neural networks and deep learning, supervised and unsupervised learning, and dimensionality reduction. Exploration of mainstream applications of machine learning to problems of chemical interest, including molecular simulation and computer-aided drug and material design/discovery. Succinct introduction to linear algebra and programming in Python. Particular topics to be covered and projects to be completed may be decided in part based on student interest and input.

P/NP or letter grading.