Practical Lab SS 23 Practical Lab Numerical Algorithms
Algorithms in Machine Learning and Their Application
The practical lab is full. In case you want to participate without a grade and support from us, you are welcomed to do that.
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.
- 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.