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Practical Lab SS 24 Practical Lab Numerical Algorithms

Algorithms in Machine Learning and Their Application

Under direction of
Dr. Bastian Bohn and Prof. Jochen Garcke
Wednesdays, 14:15 to 16:00
Room 2.035, Friedrich-Hirzebruch-Allee 7
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After the first round of registrations there are a few places left. Everybody who registered so far should be in the ecampus course and have gotten an email. If not, please get in touch.

As there were questions, in case you want to participate without a grade and support from us, you are welcomed to attend the lectures and do the exercise sheets.


There is a limited number of places.

To pre-register or in case of questions please send an e-mail to the contact address.

Please state in your pre-registration if you are a Bachelor or Master student.

We for now accept registrations till the end of February, in case of too many registrations we will randomly select. If you pre-registered, we will get in touch with you at that time.


In this practical lab, we teach the basic mathematical and technical tools needed to understand a range of basic data mining and machine learning methods. A strong emphasis is put on algorithms and efficient implementation.

Roughly every two weeks a new practice sheet is given to the participants. The tasks will be worked on in small groups. Depending on the technical proficiency, the time needed will be about 6 hours a week.


Nowadays, data mining and machine learning algorithms are the backbone of decision making processes in all major enterprises. Their applicability seems almost endless and ranges from selective advertising over prototype design to autonomous production chains. Due to the availability of very large datasets (“Big Data”) it has become crucial to understand the mechanics of the different types of learning methods and to be able to develop and implement efficient algorithms to meet the requirements of the task at hand.


Basic experience in Python is a necessary requirement. Further, the Python packages Numpy and Matplotlib will be used. The corresponding websites provide introductions which are sufficient for our purposes. All programming tasks are done using Jupyter notebooks. Should you have no experience in the mentioned tools we recommened to spend a little time familiarizing yourself with these before the course starts.

To take part for the Bachelor stream of the practical lab, formal requirements are the lectures Algorithmische Mathematik I and II. The Master stream has no formal requirements.

Selected Literature

  • Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. Springer Series in Statistics. New York, NY, USA: Springer New York Inc., 2009. online.
  • Trevor Hastie, Robert Tibshirani, Gareth James, and Daniela Witten. An Introduction to Statistical Learning (2nd ed.) Springer, 2021. online.
  • Kevin P. Murphy. Probabilistic Machine Learning: An Introduction, MIT Press, 2022. Online.