This course offers a comprehensive overview of both classical and modern statistical learning methods with a strong emphasis on their application in finance. The classical approaches covered include linear regression, logistic regression, and k-Nearest Neighbors (k-NN), providing foundational tools for prediction and classification. The course will also explore modern methods such as decision trees, ensemble techniques (boosting and bagging), support vector machines, and neural networks, as well as advanced topics like model assessment, feature selection, and unsupervised learning techniques like clustering. Throughout the course, students will apply these methods to real-world financial datasets, gaining hands-on experience in statistical learning as it pertains to asset pricing, portfolio optimization, and other key areas in finance.