Students are able to

• understand the statistical and mathematical foundations of machine learning, and can apply basic methods and techniques
• decide how and when to apply which machine learning methods; in addition, they can explain the importance and power of feature engineering, data preparation, and model evaluation
• implement basic data science methods using Python or R

No data available

The module covers the following topics/contents:
Foundations of machine learning:

• Mathematics for machine learning
• Overview of discrete and continuous probability distributions
• Basics of statistical learning and model selection
• Basics of multivariate statistics

• Introduction to primary approaches to machine learning
• Machine learning concepts and techniques
• Anatomy of a learning algorithm & overview of fundamental algorithms
  • Supervised and unsupervised learning
• Data cleaning and data preprocessing
  • Filtering techniques
  • Feature engineering
• Model inference and prediction
• Model evaluation

• Introduction to data science and machine learning with R or Python

Müller A.C., Guido S.: Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly UK Ltd, 2016.
Shalev-Shwartz S., Ben-David S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014
Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer 2009
Ramasubramanian K., Moolayil J.: Applied Supervised Learning with R. Packt Publishing, 2019.

Lecture, practical lab exercises, documentation and presentation,
problem-based learning

Integrated module examination
Immanent examination character:
Active Participation in class, short tests, lab assignments, presentations, final exam