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Foto: Universität Paderborn, Jörg Ullmann Show image information
Foto: Universität Paderborn, Jörg Ullmann Show image information
Foto: Universität Paderborn, Jörg Ullmann Show image information

Foto: Universität Paderborn, Jörg Ullmann

Foto: Universität Paderborn, Jörg Ullmann

Foto: Universität Paderborn, Jörg Ullmann

Statistical and machine learning

Two-Layer neuronal network

Short Description

The course on statistical and machine learning presents an introduction into the components and algorithms prevalent in statistical pattern recognition. Both parametric and non-parametric density estimation and classification techniques will be presented, as well as supervised and unsupervised learning paradigms. The presented techniques can be applied to a variety of classification problems, both for one-dimensional input data (e.g., speech), two-dimensional (e.g., image) or symbolic input data (e.g., documents).


  • Decision rules: Bayes decision rule, Discriminants and surface boundaries, Discriminants for multivariate gaussians, Error rates of the Bayesian classifier, Example: Soft-feature speech recognition
  • Supervised training: Mean and covariance of a gaussian distribution, Bayesian learning
  • Linear dimension reduction: Principal component analysis, Linear discriminant analysis
  • Linear discriminants: Least squares and LDA, LMS, Support vector machines, Example: MLLR speaker adaptation
  • Multilayer perceptron: Classification with neuronal networks, Training of neuronal networks
  • Unsupervised training: Mixture densities, Clustering methods

Learning Outcomes, Competences

After completion of the course students will be able to

  • choose an appropriate decision rule for a given classification problem
  • Apply supervised or unsupervised learning techniques to data of various kinds and critically assess the outcome of the learning algorithms
  • work with dedicated pattern classification software (e.g., for artificial neural networks, support vector machines) on given data sets and optimize parameter settings
  • find, for a given training set size, an appropriate choice of classifier complexity und feature vector dimensionality

The students

  • have gathered sufficient proficiency in Matlab, well beyond what is needed to realize pattern classification techniques
  • can assess the importance of the principle of parsimony and are able to transfer it to other
  • are able to apply the knowledge and skills learnt in this course to a wide range of disciplines
  • can work cooperatively in a team and subdivide an overall task into manageable subtasks and work packages


  • Lectures predominantly using the blackboard or overhead projector, occasional presentations of (PowerPoint) slide,
  • Exercise classes with exercise sheets and demonstrations on computer and
  • Implementation of learning and classification algorithms on a computer by the students themselves; use of algorithms on real-world data or data generated on the computer, evaluation of the simulation results

Proposed Literature

  • R.O. Duda, P.E. Hart und D.G. Stork: "Pattern Classification", 2nd Edition,Wiley, 2000
  • K. Fukunaga, Statistical Pattern Recognition, Academic Press, 1990
  • Hastie, Tibshirani, The Elements of Statistical Learning, Springer 2003
  • McLachlan G.J., Discriminant analysis and statistical pattern recognition, John Wiley&sons, 1992
  • Radford M., Bayesian Learning for Neural Networks, Springer 1996
  • Schürmann J., Pattern classification,a unified view of statistical...,John Wiley & sons, 1996
  • Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag 2000
  • Vapnik, Statistical Learning Theory, John Wiley 1998
  • Vidyasagar M., Learning and Generalization, Springer 2003

General Information

  • Course for master students
  • ECTS: 6
  • Language: German or English (depending on preference of students)
  • Semester: Sommersemester

The University for the Information Society