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| The lecture and practice sessions of this module are complemented by a Math refresher course, an introduction to python programming for machine learning and a seminar ("Applications of Cognitive Algorithms") for a more in-depth treatment of selected applications. | |
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| === Termine und Dozenten === | Students will implement and apply machine learning algorithms on real data. |
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| ||<|2> '''Termin:''' || Vorlesung: Mi. 12:00 - 14:00, 24.10.2012 - 13.02.2013|| || '''Raum:''' FR 3002 || || '''Verantwortlicher:''' || [[http://ml.cs.tu-berlin.de/en/klaus/index.html|Prof. Dr. Klaus-Robert Müller]] || || '''Betreuer:''' || [[mailto:felix.biessmann@tu-berlin.de|Dr. Felix Bießmann]]|| |
=== Dates === ||<|2> '''Date:''' ||Thursday, 10:00 - 12:00, 11.04.2013 - 11.07.2013|| || '''Room:''' MAR 4.064 || || '''Responsible:''' || [[http://ml.cs.tu-berlin.de/en/klaus/index.html|Prof. Dr. Klaus-Robert Müller]] || || '''Contact Person:''' || [[mailto:irene.winkler@tu-berlin.de| Irene Winkler]]|| |
Lecture "Kognitive Algorithmen"
Computer programs can learn useful cognitive skills. This lecture tries to communicate an intuitive understanding of elementary concepts in machine learning, their historical development and their application on real data with a special focus on methods that are simple to implement.
The lecture and practice sessions of this module are complemented by a Math refresher course, an introduction to python programming for machine learning and a seminar ("Applications of Cognitive Algorithms") for a more in-depth treatment of selected applications.
Students will implement and apply machine learning algorithms on real data.
Dates
Date: |
Thursday, 10:00 - 12:00, 11.04.2013 - 11.07.2013 |
Room: MAR 4.064 |
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Responsible: |
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Contact Person: |
Topics
We will cover (among other things)
- Supervised learning (linear regression techniques, linear classification, kernel based regression)
- Artificial Neural Networks (Reichardt Correlator, Perceptron Algorithm, Multilayer Neural Networks)
- Unsupervised Learning (Principal Component Analysis, Clustering)
- Model Selection
More information can be found on the ISIS Website