This course begins with an introduction to the basic concepts of statistical learning, which is the cornerstone of modern statistics. This course also lays a good foundation for further study in other areas such as big data, deep learning, and artificial intelligence. This course also highlights many of the methods of modern statistics. Statistical deep learning can be applied to many subject areas. The basic teaching objectives are to master such as principal component analysis, regularized regression analysis, LASSO-type variable selection, sufficient dimension reduction, decision tree, and classification methods. The basic goal is to teach students to master statistical learning and modern statistical methods, to develop students' statistical thinking and ability to analyze data, and to lay a good foundation for follow-up courses.