Signal Processing and Machine Learning
Target Audience: Master EI and MSCE
Next Exam: 2020-07-30 08:00 (no responsibility is taken for the correctness of this information)
Additional Information: TUMonline and Moodle
Introduction of advanced mathematical methods, concepts, and algorithms for selected topics in signal processing and machine learning and their application in current cutting-edge research in communications and data processing applications, which highlights a joint perspective on both paradigms. Introduction into the basics of estimation and classification theory, support vector machine and kernel methods, random forests, neural networks, deep neural networks, recurrent neural networks, sparse signal processing and compressive sensing for machine learning. The usage of popular toolboxes will be demonstrated in selected application examples.
Mathematical concepts and numerical algorithms for selected topics in signal processing and machine learning are introduced during the lectures. They are transferred by means of case studies and applications which demonstrate the use of the introduced concepts and their respective numerical algorithms. The students further investigate the introduced concepts by solving specific problem formulations and by applying and programming own numerical algorithms and available toolboxes.