Chair of Artificial Intelligence and Machine Learning
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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.

Prerequisites

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

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General Information

Lecturer:

Eyke Hüllermeier

Language:

German

Time and place:

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

Scope:

2 SWH Lecture +2 SWH Exercises

Target audience:

Bachelor of:

  • Informatics
  • Mathematics
  • Statistics

Assistant:

Clemens Damke

Literature

  • 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. https://mml-book.com.