Felix Geyer works as a student assistant at the Chair of Empirical Economics. At the same time he is engaged at the Chair of Econometrics at the University of Regensburg.
In his courses, he teaches students the basics to understand Machine Learning algorithms as well as how to develop and apply them on their own. He dealt with the mathematical functions of Machine Leanring algorithms in his bachelor thesis and in some seminar papers during his studies.
Chair of Empirical Economics & Chair of Econometrics
I am working at the Chair of Cassar (Emp. Economics) and the Chair of Tschernig (Econometrics).
I’m teaching “Introduction to Econometrics” and “Time Series Econometrics”. Both subjects are in the field of econometrics – a mixture of math and statistics.
In the courses, on the one hand, the basics are taught in order to be able to understand the algorithms of Machine Learning or to be able to compile them independently. On the other hand, knowledge in the field of programming is imparted in order to be able to apply Machine Learning algorithms yourself if necessary.
Prior knowledge in the field of statistics and matrix calculation is strongly recommended. The references are conveyed through a lecture and accompanying exercises. Although both lectures and exercises sometimes require a very high mathematical background.
Ability to program, understanding of the fundamental functioning of Machine Learning methods – and also points of criticism/weaknesses thereof
I think that nowadays there are more and more interconnectednesses between computers (autonomous driving) and knowledge is in great demand on the job market.
In both bachelor and seminar theses I dealt with the mathematical functionality of various ML algorithms.
Studies and self-study (self-interest)
Question 10: Do you personally find it easy or difficult to deal with topics that are related to AI?
Easy because of self-interest
How the ML algorithms work and their data usage
I think it would be truly conducive to make the AI access easier for students, e.g. through project days or programming clubs
Teaching the subjects
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Mr. Geyer, thank you very much for taking the time to answer these questions. We wish you a nice day! Mr. Frummet, thank you very much for taking the time to answer these questions. We wish you a nice day!
Mr. Geyer, thank you very much for taking the time to answer these questions. We wish you a nice day! Mr. Frummet, thank you very much for taking the time to answer these questions. We wish you a nice day!