Statistical Machine Learning

SoSe 2021

This course provides a more in-depth introduction into statistical methods in machine learning.


Course materials (Moodle) TBA
Module no. 20-00-0358-iv
Event type integrated course (IV4, 6CP, 4SWS), elective
Being taught regularly, usually summer semesters
Time TBA
First class TBA
Location TBA
Lecturer Prof. Stefan Roth, Ph.D.
Dr. Simone Schaub-Meyer
Assistants TBA
Exam TBA


  • Statistical methods in machine learning
  • Statistics, optimization, and linear algebra
  • Bayesian decision theory
  • Density estimation
  • Non-parametric models
  • Mixture models and the EM-algorithm
  • Linear models for classification and regression
  • Statistical learning theory
  • Kernel methods for classification and regression

After successfully attending the course, students have developed a more in-depth understanding of statistical methods in machine learning.

Can be taken for credit toward BSc / MSc Informatik, MSc Visual Computing, MSc Autonome Systeme, BSc / MSc Computational Engineering and others. Students from other departments, e.g. Mathematik, Elektrotechnik, IST, or Physik are welcome, though academic credit may need to be arranged.

It is recommended to have taken Math classes from the bachelor's degree and to have basic programming abilities.

  • C.M. Bishop, “Pattern Recognition and Machine Learning”, Springer 2006, Online textbook
  • K.P. Murphy, “Machine Learning: a Probabilistic Perspective”, MIT Press 2012, Online textbook
  • D. Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University Press 2012, Online textbook
  • T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning”, Springer 2003, Online textbook
  • D. MacKay, “Information Theory, Inference, and Learning Algorithms”, Cambridge University Press 2003, Online textbook
  • R.O. Duda, P.E. Hart, and D.G. Stork, “Pattern Classification (2nd ed.)”, Willey-Interscience 2001, Online textbook
  • T.M. Mitchell, “Machine Learning”, McGraw-Hill 1997, Online textbook