Uni Tuebingen
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Mathematical Introduction to Data Science

Description

A mathematically rigorous survey of core data-science techniques — from regression and k-nearest neighbors to high-dimensional phenomena, dimensionality reduction, and modern classifiers (Perceptron, SVMs, Kernel Methods, Neural Networks). Emphasis on both theoretical foundations and hands-on Python implementation with real datasets.

News

07.10.2025Lecture time on Tuesday, 12:00-14:00.
Tutorial time on Friday, 14:00-15:00.

Important Links

Lecture Slides

Exercise Sheets

Prerequisites

  1. Multivariable Calculus
  2. Linear Algebra (eigenvalues and SVD)
  3. Basic Probability Theory
  4. Python programming (NumPy, scikit-learn)

References

    Sven A. Wegner, Mathematical Introduction to Data Science


Contact person: Dr. Abishek Chaudhary. If you have any questions, please come by my office or write me an email.

 

Last modified: Monday, 24-Nov-2025 12:20:01 CET, Webmaster