Numerical Methods for Data Science



2 SWS lecture + 2 SWS exercise course

Time and Location


Thursdays 2:30pm - 4:00pm via online conference starting April 23rd

Exercise course

Fridays 12:30pm - 2:00pm via online conference starting April 24th

We will update this part with more information when we have fixed the details on how the classes will be held.

Please register in the LSF!



The class covers several powerful numerical linear algebra techniques that are used in various applications in data mining and pattern recognition. We first review basic linear algebra concepts and matrix decompositions, in particular the LU and the QR decomposition and use these techniques to solve linear systems and least square problems. Furthermore, we study different algorithms for computing eigenvalues and the singular value decomposition. Finally we will see how these concepts are used in different applications such as text mining, page ranking and face recognition. Throughout the course, the presented methods will be illustrated by test problems that are carried out in Matlab or Python.

Exercise course

The lectures are accompanied by exercise courses in which the students apply the different numerical methods that are covered in the lectures. Exercise problems are solved by the students independently, and are afterwards presented and discussed in the exercise courses.

Exercise sheets (pdf)

Please upload your solutions in the ILIAS.

Additional Material

Lecture notes


· Datenschutzlast modified on: 24 April 2020 at 2:37pm.