The digitalization of processes and environments is difficult challenge for computer scientists. Software development is hereby not the primary problem, rather the professional processing and analysis of different datatypes and volumes. For this purpose it is essential to have a certain fundamental experience in the area of data analysis and the most important methods in the domain of machine learning.
This module provides you a practical introduction in elemental data mining with methods of machine learning. The focus is on an good overall view and a clean methodology; proofs and details of the methods are not part of this course and are expected to be discussed in later course. The content of the lecture is applied practically by the participants in several projects.

Accompanying Assignments

The lecture is accompanied by practical assignments containing of implementations in python and related tools and libraries with real-world datasets.

• Script
• Slides of the lecture
• Additional material to the practical assignments

• ​Ian H. Witten, Eibe Frank, “Data Mining – Practical Machine Learning Tools and Techniques”, 4^th Edition, Elsevier, 2016.

• Programming 1&2: Reliable control of a higher procedual or object-oriented programming language
• Linear Algebra: Vector- and Matrix calculations, inverse Matrix, eigendecomposition
• Statistic and Stochastic: Fundamentals of probability calculation, distributions, correlations
• Algorithms and Data Structures: Algorithmic Thinking