0000000672 20W 2SWS SE Computational Aspects of Machine Learning (IN2107, IN0014, IN2183)   Hilfe Logo

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Computational Aspects of Machine Learning (IN2107, IN0014, IN2183) 
Winter semester 2020/21
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Informatics 5 - Chair of Scientific Computing (Prof. Bungartz)
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* Description*
Machine Learning is a rapidly growing area of research on the intersaction of applied mathematics, informatics and computational science. With advances in the machine learning theory and algorithms as well es with increasing amount of data and complexity of the models, development of fast, efficient and scalable algorithms increasingly gains importance. Hereby the range of applied techniques spreads from exploiting embarasingly parallel tasks in a data-centric fashion to the approximate solution with satisfying error limits.

In the seminar we are going to focus on the advanced methods of machine learning with particular interest in handling large-scale problems. While some topics would deal with the complete learning algorithms, others would focus on efficient solution of subtasks common for many different algorithms, e.g. nearest neighbours search or MCMC sampling.
- Foundations of applied mathematics, such as linear algebra, probability theory, calculus, and convex optimisation.
- General understanding of the basic concepts of machine learning
- Programming experience in at least one programming language.
- Understanding of basic concepts of computational science is desirable
See Module Description for IN2107, IN0014, IN2183.
Für die Anmeldung zur Teilnahme müssen Sie sich in TUMonline als Studierende*r identifizieren.
Note: There will be an online pre-meeting on 14.07.2020 between 13:00 - 14:00 at: https://bbb.in.tum.de/sev-u77-pu6

Registration through the Matching System.
Please visit the e-learning course (moodle) for more information.
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e-learning course (moodle)