Regression and Classification

This lecture covers the concepts of regression and classification, where regression models the output variable based on input variables, while classification predicts categories. It explains linear regression, transformations, generalized linear models, logistic regression, and Poisson regression. The instructor discusses the limitations of linear models, the importance of finding optimal parameters, and the application of linear regression in different scenarios. Additionally, the lecture introduces decision trees, random forests, and the concept of information gain in decision-making processes.