This course aims to introduce the mathematical principles of machine learning. The course is roughly divided into two parts: the statistical principles, and the optimization principles. For the former, we will focus on introducing basics of statistical learning theory, where the main focus is what can be learned and how good can be learned. For the latter, we will focus on algorithmic aspects of optimization, which play a key role in machine learning. As for theoretical topics, tentatively we aim to cover VC dimension, Rademacher complexity, and oracle complexity. As for learning models, we aim to cover SVM, neural networks, and boosting.