Allgemeine Informationen
| Untertitel | |
| Veranstaltungsnummer | 6061V |
| Semester | WiSe 22/23 |
| Aktuelle Anzahl der Teilnehmenden | 275 |
| erwartete Teilnehmendenanzahl | 100 |
| Heimat-Einrichtung | Professur für Angewandtes Maschinelles Lernen |
| Veranstaltungstyp | Vorlesung in der Kategorie Lehre (mit Prüfung) |
| Erster Termin | Di., 18.10.2022 12:00 - 14:00 Uhr, Ort: (WIWI) HS 8 |
| Art/Form | |
| Literatur |
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016. Charu, C. Aggarwal. Neural networks and deep learning: a textbook., 2018. |
| Qualifikationsziele |
Kenntnisse / Skills/Knowledge: Students will get to know about fundamentals of artificial neural networks, gain an overview on standard algorithms in the field as well as examples of recently proposed state-of-the-art techniques. Furthermore, students will get to know some standard tools to develop and apply deep learning techniques to machine learning problems. Fähigkeiten / Abilities: The students will be able to implement deep learning approaches to practical machine learning problems. They obtain the ability to choose and improve neural network architectures suitable for specific machine learning tasks. Kompetenzen / Competencies: Students will strengthen their competence to analyze and assess algorithms for machine learning. Participants will learn to develop problem-oriented solutions with deep learning approaches independently. |
| Workload |
60 contact hours + 120 h independent study and implementation |
| ECTS-Punkte |
6 |
