Time Wednesday, 8.15h-11.30h
Room 204
Credits 4 SWS / 5 ECTS
Exam written, 60min


  • First lesson in WS 23-24: 11.10.2023

Machine Learning

Machine Learning is currently one of the hottest topics in computer science. Almost daily we find new press releases about groundbreaking improvements in a wide field of applications, comprising e.g. Object Recognition, Speech Recognition, Digital Assistents, Robotics, Autonomous Driving, Intelligent Web-Search, Recommendation Systems, Computer Games and particularly the overwhelming applications of Generative AI and Large Language Models, such as chatGPT.

The potential of ML applications has been discovered by almost all enterprises. ML will have a strong influence on the way we work, live, communicate etc.

So, what is Machine Learning? Machine Learning is the science of building computer systems that can automatically improve with experience. In contrast to conventional computer systems, ML systems do not only process data according to a manually programmed sequence of instructions. Instead, ML systems learn and adapt the way how they process data automatically.

This lecture provides an introduction in the currently most relevant machine learning algorithms and their applications. All categories of machine learning - Supervised learning, Unsupervised learning, Reinforcement learning - are covered. Fundamental concepts e.g. on training, test and evaluation in general, are covered as well as well established conventional algorithms (e.g. Support Vector Machines) and the recently top performing Deep Learning algorithms. Actually, the focus shifts more and more from conventional ML to Deep Learning.


Video Lectures


  • Pattern recognition and machine learning (01 October 2006) by Christopher M. Bishop
  • Introduction to Machine Learning (Adaptive Computation and Machine Learning) (01 October 2004) by Ethem Alpaydin
  • Deep learning (2016) by Ian Goodfellow, Yoshua Bengio, Aaron Courville
  • Machine Learning (01 March 1997) by Tom M. Mitchell