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Geometry and Learning

This is class is about machine learning. We will cover the classical topics of classification, regression, and geometry. All these topics also turn out to be relevant for an application called conjoint analysis. We will draw most of our examples from conjoint analysis which is concerned with assessing, analyzing and representing preferences on a mutli-parameter set of options.

Lecture Notes:

Course Material:


Suggested reading:

  • Christopher Bishop. Pattern Recognition and Machine Learning. Springer Verlag
  • Trevor Hastie, Robert Tibshirani, and Jerome Friedman.The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Verlag
  • Andre Ng. Machine Learning. (lecture notes)
  • Anders Gustafsson, Andreas Herrmann, and Frank Huber (Editors).Conjoint Measurement. Springer Verlag