Multimedia Computing and Computer Vision Lab












Student Theses


Source Code / Datasets




WS 11/12: Maschinelles Lernen I

From Multimedia Computing Lab - University of Augsburg

Klausureinsicht: 22.02.2012, 10.00 - 10.30, 1020N. Bitte Ausweis und Studentenausweis mitbringen.

Klausurergebnisse können in LectureReg eingesehen werden.

Figure from C. Bishop:Pattern Recognition and Machine Learning, Springer, 2006.


Instructors: Dr. Nicolas Cebron
Time Lecture: Tue, 15:45 - 17:15, Room: 1054 N
Time Exercise: Tue, 17:30 - 19:00, Room: 1054 N
Credits: 2 + 2 SWS, Schein, yes, LP 5
Exam: Wednesday, 15.02.2012, 10:00 - 11:30, 1058N
Multimedia Teilbereiche: Multimedia Methoden, Multimedia Anwendungen, Systemnahe Grundlagen von Multimedia


  • Vorlesung wird auf Deutsch gehalten (trotz der englischsprachigen Folien und Literatur)


This lecture will provide an introduction to the fields of machine learning and pattern recognition. They are concerned with the automatic discovery of regularities in data through the use of computer algorithms. With the use of these regularities, actions such as classifying the data into different categories can be taken.

Application areas range from robot navigation, classification of spam mails to speech recognition. In this lecture, we will emphasize a Bayesian perspective.

The following topics will be covered:

  • Data Preprocessing / Visualization
  • Probability Distributions
  • Clustering
  • Linear Models for Regression/Classification
  • Neural Networks
  • Kernel Methods
  • Support Vector Machines
  • Ensemble Methods


Date Content Slides Exercise
18.10.2011 Organization / Introduction PDF PDF
25.10.2011 Data Understanding PDF PDF
08.11.2011 ML Concepts, Probabilities PDF PDF
15.11.2011 ML Concepts 2, Probability Distributions PDF PDF
22.11.2011 Unsupervised Learning PDF PDF
29.11.2011 Unsupervised Learning 2 PDF PDF
06.12.2011 Linear Models for Regression PDF PDF
13.12.2011 Linear Models for Regression 2 PDF PDF
20.12.2011 Linear Models for Classification PDF PDF
10.01.2012 Probabilistic Models / Decision Theory PDF PDF
17.01.2012 (Sparse) Kernel Methods PDF PDF
24.01.2012 Ensemble Methods: Decision Trees PDF PDF
03.02.2012 Ensemble Methods: Decision Trees 2 PDF


  1. C. M. Bishop. Pattern Recognition and Machine Learning. Springer Verlag.
  2. M.R. Berthold, C.Borgelt, F.Höppner, F.Klawonn. Guide to Intelligent Data Analysis. Springer Verlag. (