Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
Explores the application of machine learning in molecular dynamics and materials, emphasizing the creation of meaningful features and the importance of generalizability.
Explores stochastic models for communications, covering mean, variance, characteristic functions, inequalities, various discrete and continuous random variables, and properties of different distributions.