Ruprecht-Karls-Universitšt Heidelberg

Machine Learning / Maschinelles Lernen


Applications of pattern recognition:
  • detection of faces in images
  • tracking in particle physics detectors
  • tumor diagnostics by imaging mass spectrometry
  • classification of cells for fluorescent microscopy

Contents

This course introduces machine learning methods for finding patterns in data. This rapidly developing field has applications in data mining, high energy physics, computer vision and robotics, image processing for industry and life sciences, speech recognition, machine translation and many other areas.

Lectures and exercises will be interwoven and will allow you to try your hand on real-life problems and cutting-edge data from some of the application areas named above.

Formalities

Lectures: Monday, 14:00-18:00 in the HCI, Speyerer Str. 6, second floor. Lectures commence on Monday, Oct 14th, 2013.
Exercises: Details can be found here
This course accounts for 8 CP and is eligible as part of the new Physics Vertiefungsbereich "Computational Physics (MVX)".

Prerequisites

Basic linear algebra and calculus

Past curriculum and recordings (from summer 2012)


















Literature

  • The Elements of Statistical Learning (2nd ed.). Trevor Hastie, Robert Tibshirani, Jerome Friedman. Springer, 2009.
  • Pattern Recognition and Machine Learning. Christopher M. Bishop. Springer, 2006.
  • Bayesian Reasoning and Machine Learning. David Barber. Cambridge Univ. Press, 2012.
  • Pattern Classification and Machine Learning. Matthias Seeger