Prof. Dr. Felix Naumann


Dr. Ralf Krestel

Teaching Assistant

Maximilian Jenders


Data arising from business transactions, scientific measurements and other forms of content-creation calls for automatic data mining and pattern recognition techniques that allow us to efficiently make sense of this data. At the same time these techniques should be able to handle uncertainty, as data from measurements may be imprecise and user-generated content may be unreliable. This lecture will introduce the main concepts of data mining and machine learning, ranging from basic probability and information theory to popular classification, clustering, and regression algorithms.


Although we will review basic probability theory and statistics, prior knowledge in these areas are useful. Further, we will make heavy use of linear algebra and a fundamental understanding thereof is necessary.



  • Lectures:

    • Monday 13:30 in Room D-E.9/10
    • Every second Wednesday 13:30 in Room  D-E.9/10 starting 15.4.15
    • Slides will be available in the "Materialien" folder in the "Interner Bereich"

  • Exercises: 

    • Every other second Wednesday 13:30 in Room  D-E.9/10 starting 22.4.15
    • The homework assignments are available in the "Materialien" folder in the "Interner Bereich"



Introduction 13.4.15What is Data Mining?
15.4.15Binary Classification
20.4.15Beyond Binary Classification
22.4.151. Exercise Introduction
2Basics27.4.15Introduction to Statistics
29.4.15Explorative Data Analysis
6.5.152. Exercise Basics
11.5.15 Experiments
3Logical Models13.5.15Tree ModelsdecisionTrees.pdf
18.5.15Rule ModelsassociationRules.pdf
20.5.153. Exercise Logical Models
4Geometric Models27.5.15Linear Models IridgeRegression.pdf
1.6.15Linear Models IIsvm.pdf
3.6.15Distance-based ModelskMeans.pdf
8.6.154. Exercise Geometric Models
5Probabilistic Models10.6.15Introduction to Probability TheorynaiveBayes.pdf
15.6.15Gaussian ModelskernelLda.pdf
17.6.15Linear Models IIIgeneralizedLinearModels.pdf


5. Exercise Probabilistic Models
6Graphical Models24.6.15Bayes NetsbayesNets.pdf
29.6.15Markov Random Fieldscrf.pdf
1.7.15No Lecture
6.7.15Mixture Models and EMem.pdf
8.7.15Topic Modelslda.pdf
13.7.156. Exercise Graphical Models
7Combining Models15.7.15EnsemblesadaBoost.pdf
20.7.15Artificial Neural NetworksdeepLearning.pdf
22.7.157. Exercise Combining Models


There will be an oral exam at the end of the term.

Condition for admission is successful homework and exercise participation.