| While machine learning software is available to design and train rich and flexible machine learning systems, the mathematical foundations of machine learning are important in order to understand fundamental principles for creating new machine learning solutions, and learn about the inherent assumptions and limitations of the methodologies. This course will contain two parts, where Part I lays the mathematical foundations in linear algebra、analytic geometry、matrix decomposition、probability theory and optimization , and Part II applies the concepts from Part I to a set of fundamental machine learning problems including regression, dimensionality reduction, density estimation, and classification. |