Supervised Learning

Course Code : IA342

Time Hours : 18 hours

Time Periods : not available yet

Lecturer : Blaise Hanczar, Assistant Professor, blaise.hanczar[at]parisdescartes.fr

Objective

The main purpose of this course is to describe the supervised learning techniques most commonly used. Different types of data from several fields such as medical, bioinformatics, text mining will illustrate the interest of supervised learning.

Acquired Skills

Which method to use in each context, how to interpret its results on real situations, and how to select one or several methods to be used.

Contents

  •  Main steps in supervised classification
  •  The K nearest neighbors method
  •  Classification trees
  •  Linear and non linear Discriminant Analysis
  •  Logistic regression
  •  Radial Basis Functions
  •  Support Vector Machines
  •  Ensemble method

References

  •  Pattern classification, Richard O. Duda, Peter E. Hart, David G. Stork , Wiley (2000)
  •  The Elements of Statistical Learning : Data Mining, Inference, and Prediction. Trevor Hastie Robert Tibshirani Jerome Friedman. Springer (2009)
  •  Pattern Recognition and Machine Learning. Christopher Bishop. Springer (2006)