Unsupervised Machine Learning

This course expands on methods of machine learning (ML) and covers unsupervised learning in particular. Unsupervised Learning includes such methods as principal component analysis (PCS), k-means clustering, Gaussian mixture models and kernel density estimation. All of these methods will be covered extensively in the course.

Supervised Machine Learning

This course expands on methods of machine learning (ML) and covers supervised learning in particular. Supervised Learning includes such methods as linear regression, support vector machines (SVM), decision trees and random forests. All of these methods will be covered extensively in the course.

Mathematical Foundations of Machine Learning

Tools of Machine Learning

Density functional theory for computational materials design

This is the educational course inside the BOOSTalent project - «Density functional theory for computational materials design». This is the first course in a series on quantum mechanical calculations of materials. In this course, we will delve into the Density Functional Theory (DFT), a theory widely used for material property calculations in materials science, biochemistry, drug discovery, solar cells, batteries, and many other fields. This course is purely theoretical but crucial for proceeding to practical courses in quantum mechanical calculations