Teaching
Physics I
This course provides a broad foundation in mechanics and thermal physics for students who study science, engineering or related programmes.
Machine Learning in Physics
This course introduces the fundamentals of machine learning as applied to problems in physical science. Upon completion of the subject, students will be able to:
- extract, visualize and process data from online databases dedicated to materials scientists and physicists;
- select machine learning models to solve specific problems;
- implement various machine learning models in Python; and
- evaluate and improve the performance of a model.
- Week 1. Introduction
- Week 2. Data for machine learning
- Week 3. Linear regression
- Week 4. Multivariate linear regression
- Week 5. Logistic regression
Atomistic View of Matter
The course presents the physics that govern molecules and solids at the atomic scale and relate these processes to the macroscopic world. Upon completion of the subject, students will be able to:
- design, perform and analyze computer experiments using electronic and atomistic simulation techniques appropriate for the problem at hand;
- extract materials properties from the simulations;
- recognize the approximations and estimate the level of accuracy to be expected from each modeling technique, and
- critically read the current scientific literature on computational modeling and simulation of materials.
The course initially developed with Ale Strachan at Purdue is available online on nanohub. We provide videos of the lectures during (ongoing) spring 2020 semester: