Chair of Artificial Intelligence and Machine Learning

Breadcrumb Navigation


Grundlagen des Maschinellen Lernens (SS 2024)

Fundamentals of Machine Learning

Machine learning has become increasingly important in the recent past, not only as a main pillar of modern AI, but also as a method provider and an innovation driver in many application areas. This bachelor'slecture gives an introduction to the fundamental ideas and concepts of machine learning as a scientific discipline at the intersection of informatics, statistics and applied mathematics. The focus will be on the supervised learning class of problems. The exposition will cover a spectrum from the theoretical foundations of generalisation to important methodological and algorithmic concepts.


  • Mathematical fundamentals (linear algebra, calculus)
  • Algorith fundamentals (algorithms and data structures)
  • Probability and statistics fundamentals
  • Programming


General Information


Eyke Hüllermeier



Time and place:

Lecture Tuesday 14-16
Exercises 1 Thursday 10-12
Exercises 2 Friday 10-12


2 SWH Lecture +2 SWH Exercises

Target audience:

Bachelor of:

  • Informatics
  • Mathematics
  • Statistics


Clemens Damke


  • Y.S. Abu-Mostafa, M. Magdon-Ismail, H.T. Lin: Learning from Data, AML Book, 2012.
  • I. Goodfellow, Y. Begio, A. Courvill: Deep Learning, MIT Press, 2016.
  • P. Flach: Machine Learning, Cambidge Univ. Press, 2012.
  • E. Alpaydin: Machine Learning, Oldenbourg 2008.
  • C.M. Bishop: Pattern Recognition and Machine Learning, Springer 2006.
  • D.J. Hand, H. Mannila, P. Smyth: Principles of Data Mining, MIT Press 2000.
  • T. Hastie, R. Tibshirani and J. Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001.
  • T. Mitchell: Machine Learning, McGraw Hill, 1997.
  • I.H. Witten, E. Frank: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2000.
  • E. Lehman, F.T. Leighton, A.R. Meyer: Mathematics for Computer Science, 2017.
  • M.P. Deisenroth, A.A. Faisal, C.S. Ong: Mathematics for Machine Learning, Cambridge University Press, 2020.