The students know how and when to apply unsupervised learning methods. Additionally, the students know the importance and the power of feature engineering and the crucial step of model evaluation.
Further, they are able to apply the ML cycle for unsupervised ML techniques with the scripting languages R and Python.

none

The module covers the following topics/contents:
A comprehensive overview on unsupervised learning methods

• Distance and similarity measures
• Clustering methods (K-means, Fuzzy c-means, Hierarchical clustering, Spectral, DBSCAN, ..)
• Anomaly detection
• Dimension reduction techniques (PCA, ICA, Non-negative Matrix Factorization, Singular Value decomposition)
• low rank approximations
• new developments in the area of unsupervised learning

• Unsupervised ML methods in script languages (R/Python)
• Application of unsupervised ML methods to data (model setup, evaluation & prediction)

Lecture script as provided in the course (required)
Introduction to Machine Learning with Python: A Guide for Data Scientists. Sarah Guido. O'Reilly UK Ltd, 2016
Understanding Machine Learning: From Theory to Algorithms. Shai Shalev-Shwartz und Shai Ben-David. Cambridge University Press, 2014
Machine Learning for Hackers. Drew Conway & John Myles White. O'Reilly UK Ltd, 2012
The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Springer 2009

Integrated course - teaching & discussion, guestlectures by specialists, demonstration, practical examples, home work

Immanent examination character: two courseworks (one in accordance with the module Data Source and Data Quality) and written/oral exam