Machine Learning

About Machine Learning

Artificial intelligence and machine learning have long since found their way into large parts of industry. Success has most recently been achieved in autonomous driving, medical image processing or material testing. In future, Artificial Intelligence (AI) will play an even greater role in numerous industries and scenarios. To remain fit for this future, companies have to deal with the basics of AI and machine learning.

These basics are imparted in the workshop on Machine Learning, which bundles the sheer flood of information and offers you a compact overview of theory and practice in machine learning. You receive the hands-on knowledge you need to integrate the enormous potential of artificial intelligence into your product portfolio and value chain.

This workshop is very practice-oriented. Half of it consists of applied exercises on a consistent topic. You will learn the relevant methods and practices around machine learning while focusing on artificial neural networks, the basis for deep learning.



  1. Basics
  2.  Introduction and definition of terms (machine learning, artificial intelligence, big data, deep learning, …)
  3.  Presentation of the technologies used in the workshop, e.g. TensorFlow and Keras
  4. Landscape of Machine Learning Methods
  5.   Supervised learning
  6. Unsupervised learning
  7.  Reinforcement learning
  8. Single-layer neuronal networks (perceptrons)
  9.  Biological motivation
  10. From biological to artificial neurons
  11. Learning: optimization, gradient descent
  12. Classification of multiple classes
  13. Basic terms & tools
  14. Loss functions
  15. Performance metrics
  16. Data partitioning
  17.  Feature extraction, dimensionality reduction
  18. Overfitting and countermeasures
  19. Multilayer neural networks
  20. Backpropagation
  21.  Deep learning
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