Machine Learning in Molecular DynamicsExplores the application of machine learning in molecular dynamics and materials, emphasizing the creation of meaningful features and the importance of generalizability.
Regression: Interactive LectureCovers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.
Linear Regression BasicsCovers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Data-Driven Modeling: RegressionIntroduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.