Machine Learning

Covers fundamental concepts in statistical learning and machine learning. It includes topics such as linear regression, classification methods, resampling methods, model selection and regularization, non-linear models, tree-based methods, support vector machines, unsupervised learning, and more. The course provides a comprehensive introduction to the theoretical and practical aspects of statistical learning, with a focus on real-world applications and examples. It also trains ML related R/Python programming skills and exercises to reinforce learning and facilitate practical implementation of the concepts discussed. Overall, it serves as a valuable resource for students, researchers, and practitioners interested in statistical learning and its applications in various fields.