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Computational Aspects of Machine Learning (IN2107, IN0014, IN2183)

Course 0000000672 in WS 2017/8

General Data

Course Type seminar
Semester Weekly Hours 2 SWS
Organisational Unit Informatics 5 - Chair of Scientific Computing (Prof. Bungartz)
Lecturers Responsible/Coordination: Hans-Joachim Bungartz

Assignment to Modules

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks * 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. Please visit the course webpage for further information.
Links Course documents
E-Learning course (e. g. Moodle)
TUMonline entry
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