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The first lab session on Monday April 11th will give a basic tutorial on statistics.

Lecturer and Tutor

Prof. Dr. Dietmar Saupe (lecturer)

Thorsten Dahmen (tutor)


Lectures Wed 10.15 - 11.45 Z613

Fri 08:30 - 10:00 Z613
Lab Session Mon 14:15 - 15:45 Z613

Dates of Remaining Lectures (V) and Lab Sessions (Ü)

Mo Mi Fr
20.06. - 24.06.2011 Ü Ü V
27.06. - 01.07.2011 V V V
04.07. - 08.07.2011 Ü Ü Ü
11.07. - 15.07.2011 V V V

Target Audience and Prerequisites

The course belongs to the topic areas Foundations of Computer Science and Applied Computer Science and is for Master and PhD-Students (not Bachelor) of the following subjects:

  • Information Engineering and Computer Science
  • Mathematics
  • Physics

Basic knowledge in probability theory, calculus and linear algebra are expected.


  • Students must register in StudIS. In case you do not have access, please use the offline formular. Without registration you cannot sit the exam!
  • Additionally, students have to register in the LSF for this course in order to extend their account of the computer science department. This replaces the former registration by the account tool.

Students can unsubscribe for exams until one day before the first examination date. Absence does not have any consequences.


This one-semester graduate course provides a first introduction to statistical inference - theory that lies in the heart of pattern recognition (classification). The course includes the following topics of classical theory:

  • Bayesian inference,
  • parametric and nonparametric estimation of probability density functions,
  • linear classifiers,
  • classifiers evaluation,
  • feature generation and feature selection,

complemented by discussions of newer approaches, such as, support vector machines or neural networks.

Course works and credits

There are 2 lectures + 1 lab a week. At the lab sessions, students will work on theoretical and computer-based problems that constitute a part of homework assignments. The lab sheets are put online on Fridays after the lecture and are designed to roughly cover the topics of the lectures in the same week. The solved assignments must be turned in until the Monday after the next, 2pm (before lab session). If they are returned until the Friday before, 10 am, they will be marked until the following lab session, otherwise later.

The marks will affect your final grade as follows:

  • > 50 % admission for final exam
  • > 70 % 1/3 of a grade bonus on your final grade
  • > 90 % 2/3 of a grade bonus on your final grade

Matlab Access

Matlab will be used extensively during the course and is installed locally on every computer in Z 613. For completing assignments you can use the computer pool in V 304. Remote access from home might be slow. Matlab is installed on a server:

  • for Linux: /net/lin_local/matlab/bin/matlab
  • for Windows: \\titan01\lin_local\matlab\win\bin\matlab.bat



The course ends with a written final exam. Probably, there will be an intermediated take-home exam as well. The final grade will be the grade obtained at the final exam.

Literatur / Weblinks

  • Richard O. Duda, Peter E. Hart and David G. Stork.
    Pattern Classification
    (2nd ed.), 2000.

    For further supporting material, please visit the book website.
  • Chrisopher M. Bishop.
    Pattern Recognition and Maschine Learning
    , 2007.
  • Keinosuke Fukunaga.
    Introduction to Statistical Pattern Recognition
    (2nd edition), 1990.
  • Sergios Theodoridis, Konstantinos Koutroumbas.
    Pattern Recognition
    (3d edition), 2006.