Mathematical Foundations of Data Science

Course content

The students understand the of the complex links between their previous mathematical knowledge and the contents of the lecture, understand the theoretical body of the lecture as a whole and master the corresponding methods, are able to analyze and apply the methods of the lecture, understand the applied methods and are able to analyze these, master the foundations of the field
are able to them into a larger context.

Content

• foundations of supervised learning, different loss functions and risk analysis
• Regression-and Classification problems in reproducing kernel Hilbert spaces
• empirical risk minimization, regularization, Gradient Descent and rates of convergence