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Research Programs
Einführung in die Wissensverarbeitung
General Information
This course is held in two parts: The first part is taught by Stefan Häusler (VO) and Stefan Habenschuss (UE) from the Institute of Theoretical Computer Science, the second part by Franz Pernkopf (VO) and Paul Meissner (UE) from the Institute of Signal Processing and Speech Communication.
Aims and objectives of the course: Knowledge of the most important concepts and methods form the areas machine learning, neural networks, statistical modelling and classification.
Covered Topics:
- Introduction to Machine Learning
- Learning Algorithms for Neural Networks
- Algorithm independent Machine Learning
- Practical Classification Algorithms
- Unsupervised Learning
- Hidden Markov Models
- Graphical Models
Prerequisites
There are no particular courses which must be taken as prerequisites for this course. Although there will be an introduction to MATLAB in the beginning of the exercises, it is recommended to have already some basic knowledge and experience in it. We also assume elementary mathematical knowledge in probability theory, statistics, analysis and calculus.
Lecture Material
Here you can find material for the lecture course.
Bibliography and further reading tipps are found at the bottom of this page.
Homework Assignments and Rules
You can find the assignments and corresponding information here.
Online Tutorials
Here you can find the online tutorials.
Problem classes slides and material
Here you can find slides and material for the problem classes.
Bibiliography and Further Reading
Reading tipps and a bibliography are found here.