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Mathematic(al Statistic)s and Applications of Machine Learning

Dr. Dirk-André Deckert

Schedule and Venue


Dr. Dirk-André Deckert

Mon 14-16

Wed 14-16

First Lecture: Mon 12 April 2021



Mariia Seleznova

Tue 14-16



Course Description

This course will provide an introduction into selected topics on machine learning. We will start from the basics, e.g., perceptrons, adalines, support vector machines, and proceed to multi-layered neural networks and deep learning. The minimum goal is to arrive at an understanding of both the mathematics and the implementation of the now standard handwritten numbers recognitional problem by means of neural networks. The mathematical discussion will focus on machine learning as on statistical optimization and approximation problem. As regards applications, it is the objective of the tutorials to implement several applications of the discussed algorithms in Python. Depending on interest and time, we may select from more advanced topics such as vector embeddings, convolution and recurrent networks, decision trees, reinforcement learning, ensemble methods, and boosting.


Please find the information on the course page at UNI2WORK.

For whom is this course?

Target Participants: Master students of Mathematics, Financial Mathematics, and TMP

Pre-requisites: This course is offered having mainly master students in Mathematics, Financial Mathematics, and TMP with corresponding prior knowledge, in particular, also in Probability Theory in mind. A basic knowledge in Python or a similar programming language and access to a computer with a Python development environment is required in order to complete the tutorials.

Applicable credits: This course is offered in two versions:

  • Mathematics and Applications of Machine Learning (2+2h = 6 ETCS)
  • Mathematical Statistics and Application of machine Learning (4+2h = 9 ETCS)

Both courses share the 2+2 hours of the first. The second course extends the first by another 2h with a deeper dive into statistics. There will be a shorter and longer exam for the 6 and 9 ETCS version, respectively. Only one of the courses is credible.

  • Master Financial and Insurance Mathematics: According to PO 2019, the 6 ETCS Version may be credited as WP12 or WP24, while the 9 ETCS one as WP21 or WP23.
  • Master in Mathematics: According to PO 2011, the 6 ETCS Version may be credited as WP47.2 (Vorlesung) und WP47.3 (Übung), while the 9 ET