| Machine Learning in Electrical Engineering and Information Technology |
| Semester: |
| SoSe 24 |
| Veranstaltungstyp: |
| Vorlesung (Lehre) |
| Veranstaltungsnummer: |
| lv3004_s24 |
| DozentIn: |
| Prof. Dr. sc. techn. Christian Schuster, Prof. Dr.-Ing. Christian Becker, Prof. Dr. Alexander Kölpin, Gerhard Bauch, Dr. Maximilian Stark, Dr. Davood Babazadeh, Dr. Cheng Yang, PD Dr.-Ing. habil. Rainer Grünheid, Simon Stock, M.Sc. |
| Beschreibung: |
| This master course, a collaborative effort between the Institute of Communications, the Institute for High-Frequency Engineering, the Institute for Power Systems, and the Institute for Theoretical Electrical Engineering, is designed to unveil the synergies between machine learning and our respective fields of expertise.
In an age defined by rapid technological advancement, machine learning stands as a catalyst for innovation, offering transformative possibilities across diverse sectors. From optimizing communication systems to enhancing power grid efficiency, and from refining signal processing techniques to enabling autonomous systems, the integration of machine learning techniques holds immense promise for addressing contemporary challenges.
Throughout this course, we will delve into the theoretical underpinnings, practical methodologies, and tangible applications of neural networks and machine learning algorithms. By delving into algorithmic design, data analysis, and optimization techniques, we aim to equip you with the skills and insights needed to navigate the complexities of modern engineering landscapes. |
| Leistungsnachweis: |
| m1785-2022 - Machine Learning in Electrical Engineering and Information Technology<ul><li>p1778-2022 - Machine Learning in Electrical Engineering and Information Technology: mündlich</li></ul> |
| ECTS-Kreditpunkte: |
| 6 |
| Weitere Informationen aus Stud.IP zu dieser Veranstaltung |
Heimatinstitut: Institut für Nachrichtentechnik (E-8)
In Stud.IP angemeldete Teilnehmer: 102
Anzahl der Postings im Stud.IP-Forum: 2
Anzahl der Dokumente im Stud.IP-Downloadbereich: 1
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