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Integrated Lecture "Cognitive Algorithms"

Computer programs can learn useful cognitive skills. This integrated lecture tries to communicate an intuitive understanding of elementary concepts in machine learning and their application on real data with a special focus on methods that are simple to implement. We recommend the "Machine Learning 1" lecture or the "Machine learning lab course" for a more advanced treatment (this course is not a prerequisite).

We will alternate a lecture and a practice session. In the practice session students will implement and apply machine learning algorithms on real data in Python.



Wednesday, 10:00-12:00


MAR 0.011


Prof. Dr. Klaus-Robert Müller


Dr. Wojciech Samek (wojciech.samek@hhi.fraunhofer.de)

Stephanie Brandl (stephanie.brandl@tu-berlin.de)

Contact Person:

Stephanie Brandl


We will cover (among other things)

  • Supervised learning (linear regression techniques, linear classification, kernel based regression, neural networks)
  • Unsupervised Learning (Principal Component Analysis, Clustering)
  • Model Selection

More information can be found on the TBA Website.




The following prerequisites are helpful for taking the course:

  • Basic knowledge in linear algebra and calculus
  • Basic programming knowledge, programming in Python


The integrated lecture is the compulsory part of the B.Sc. module "Kognitive Algorithmen" in Computer Science. This "Kognitive Algorithmen" module is a 6 ECTS/SP module, and consists of

The grade will be determined in a written exam at the end of the semester. The grades of the elective will not count towards the grade of the entire module.

IDA Wiki: Main/SS18_KA (last edited 2018-08-01 12:20:50 by StephanieBrandl)