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Courses in the winter semester 2021/22

Please note that due to the IT attack on the TU Berlin, it is currently not possible to edit this website. So unfortunately not all information is kept up to date.

You can therefore find our course offer in the winter semester 2021/22 here.

Reduced course portfolio due to COVID-19 pandemic

Due to the ongoing COVID-19 pandemic the Neural Information Processing group might not offer all courses for the SoSe 2021. Further information of the execution can be found on ISIS.

The following courses will be offered:

- Praktisches Programmieren und Rechneraufbau 

- Machine intelligence II 

- Einführung in die Informatik . Vertiefung 

- NI-Projekt

The following courses might take place (not decided yet):

- Advanced topics in reinforcement learning

Neural Information Processing Group

We are concerned with the principles underlying information processing in biological systems. On the one hand we want to understand how the brain computes, on the other hand we want to utilize the strategies employed by biological systems for machine learning applications. Our research interests cover three thematic areas.

Models of Neuronal Systems:


In collaboration with neurobiologists and clinicians we study how the visual system processes visual information. Research topics include: cortical dynamics, the representation of visual information, adaptation and plasticity, and the role of feedback. More recently we became interested in how perception is linked to cognitive function, and we began to study computational models of decision making in uncertain environments, and how those processes interact with perception and memory.

Machine Learning and Neural Networks:


Here we investigate how machines can learn from examples in order to predict and (more recently) act. Research topics include the learning of proper representations, active and semisupervised learning schemes, and prototype-based methods. Motivated by the model-based analysis of decision making in humans we also became interested in reinforcement learning schemes and how these methods can be extended to cope with multi-objective cost functions. In collaboration with colleagues from the application domains, machine learning methods are applied to different problems ranging from computer vision, information retrieval, to chemoinformatics.

Analysis of Neural Data:


Here we are interested to apply machine learning and statistical methods to the analysis of multivariate biomedical data, in particular to data which form the basis of our computational studies of neural systems. Research topics vary and currently include spike-sorting and the analysis of multi-tetrode recordings, confocal microscopy and 3D-reconstruction techniques, and the analysis of imaging data. Recently we became interested in the analysis of multimodal data, for example, correlating anatomical, imaging, and genetic data.

Selected Publications

Support Vector Machines for Dyadic Data
Citation key Hochreiter2006b
Author Hochreiter, J. and Obermayer, K.
Pages 1472 – 1510
Year 2006
DOI 10.1162/neco.2006.18.6.1472
Journal Neural Comput.
Volume 18
Abstract We describe a new technique for the analysis of dyadic data, where two sets of objects (\"row\" and \"column\" objects) are characterized by a matrix of numerical values which describe their mutual relationships. The new technique, called \"Potential Support Vector Machine\" (P-SVM), is a large-margin method for the construction of classifiers and regression functions for the \"column\" objects. Contrary to standard support vector machine approaches, the P-SVM minimizes a scale-invariant capacity measure and requires a new set of constraints. As a result, the P-SVM method leads to a usually sparse expansion of the classification and regression functions in terms of the \"row\" rather than the \"column\" objects and can handle data and kernel matrices which are neither positive definite nor square. We then describe two complementary regularization schemes. The first scheme improves generalization performance for classification and regression tasks, the second scheme leads to the selection of a small, informative set of \"row\" \"support\" objects and can be applied to feature selection. Benchmarks for classification, regression, and feature selection tasks are performed with toy data as well as with several real world data sets. The results show, that the new method is at least competitive with but often performs better than the benchmarked standard methods for standard vectorial as well as for true dyadic data sets. In addition, a theoretical justification is provided for the new approach.
Bibtex Type of Publication Selected:main selected:structured selected:publications
Link to publication Link to original publication Download Bibtex entry

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Prof. Dr. rer. nat. Klaus Obermayer
Room MAR 5043

consultation hours:
Wed 12am-1pm
registration via email

During the restricted acces to TU buildings in reacion to the Covid-19 pandemic, it is nescessary to register per email for the office hour of Prof. Obermayer.

Please send an email with some days in advance to explain your concern. If it is not possible to solve it by email, you will receive an email at the time of the office hour (Wed, 12-1 pm) including a link which will allow to participate in a video conference with Prof. Obermayer.

All requets will be handled first-in-first-out. Please stay tuned for the whloe time of the office hour.

Administrative Office
Groiss, Camilla
Room MAR 5042
Fon: +49 30 314 73442
Fax: +49 30 314 73121

Consultation hours:
We 9am - 11am