Mathematical Principles of Machine Learning is a graduate level course (motivated undergraduate students are also welcome) designed for students who are interested in the theoretical foundations of machine learning. Machine learning are computational methods that leverage past experiences (data) to improve performance in making predictions and taking actions. The design of machine learning methods leads to three fundamental questions regarding representation, optimization, and generalization. In this course, we aim to introduce the underlying mathematical principles of concurrent solutions and answers to these three questions.