Machine Learning

Lecturer: Prof. Dr. Christian Ressel
Lecturer: Prof. Dr. Kai Essig

Course material: HSRW - Moodle - Section

Machine Learning is a key to develop intelligent systems and analyze data in science and engineering. This course provides an introduction to the fundamental methods at the core of machine learning. It covers theoretical foundations including supervised and unsupervised learning. The theory is being introduced by examples from the ambient intelligence domain (and others) including learning as an approach to realize adaptive systems. The implementation of programs in this domain is also part of the course. Having passed this module students are aware of the challenges of machine learning. They have gained basic understanding not just of algorithms but also critical reflection, which allows them to perform problem oriented feature engineering, to find appropriate models.

Some content to get a taste

Course and its content will be offered soon


  • Introduction and motivation
  • Adaptive systems
  • Statistical learning theory
  • Local methods, like: nearest neighbor, k-nearest neighbor, parzen windows, high dimensions
  • Bias variance and cross validation
  • Regularized least squares and classification
  • Regularization Networks
  • Kernel based methods and support Vector Machines
  • Dimensionality reduction and variable selection
  • Clustering algorithms
  • Neural Networks
  • Bayesian learning