Mathematical Data Analysis

What it is about

You will learn methods for analyzing complex and unstructured data, including regression and classification tasks. The module provides fundamentals such as loss functions, positive definite kernels and reproducing kernel Hilbert spaces as well as regularization strategies (e.g. Tikhonov) and SVM regression. In addition, summation procedures and manifold learning are covered.

What you will be able to do

• Basic understanding of the analysis of complex and unstructured data
• Knowledge of learning concepts and loss functions
• Familiarity with reproducing kernel Hilbert spaces and positive definite kernels
• Understanding of regularization theories (e.g. Tikhonov) and related problems
• Application of SVM regression and methods of manifold learning
• Use of summation or summability methods

What the module consists of

• VorlesungVermittlung der theoretischen Grundlagen
• Integrierte ÜbungenApplication and practice of the content; students shall work on problems, solutions will be presented in the course

Teaching method

• Vortrag (Tafel/Präsentation)Explanation of concepts and derivations
• Übungsbearbeitung mit LösungsgesprächenConsolidation of understanding through active problem solving