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Machine Learning for Engineers II: Advanced Methods (MLE2)2.5 ECTS (englische Bezeichnung: Machine Learning for Engineers II: Advanced Methods)
Modulverantwortliche/r: Björn Eskofier Lehrende:
Björn Eskofier, Jörg Franke, Nico Hanenkamp
Startsemester: |
WS 2021/2022 | Dauer: |
1 Semester | Turnus: |
halbjährlich (WS+SS) |
Präsenzzeit: |
0 Std. | Eigenstudium: |
75 Std. | Sprache: |
Englisch |
Lehrveranstaltungen:
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- Machine Learning for Engineers; Advanced Methods and Tools (Vorlesung mit Übung, Online)
Advanced Methods and Tools
Empfohlene Voraussetzungen:
Es wird empfohlen, folgende Module zu absolvieren, bevor dieses Modul belegt wird:
Machine Learning for Engineers; Introduction to Methods and Tools (SS 2021)
Inhalt:
This course focuses on various aspects of Deep Learning. Theoretical foundations and general concepts are introduced in the first part, while the second part focuses on specific networks used in image analysis as well as time-series analysis, two common tasks in engineering applications.
The list of topics covered includes:
In the integrated lab sessions, the students will tackle an image classification problem as well as a time-series regression problem using industrial datasets.
Lernziele und Kompetenzen:
- Wissen
- Students are able to recapitulate different machine learning methods and algorithms.
- Anwenden
- Students are able to choose and implement a suited deep learning algorithm for a given problem based on the type of data and the general learning task.
Literatur:
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, Springer, 2009
Studien-/Prüfungsleistungen:
Machine Learning for Engineers II: Advanced Methods (Prüfungsnummer: 50681)
(englischer Titel: Machine Learning for Engineers II: Advanced Methods)
- Prüfungsleistung, Klausur, Dauer (in Minuten): 60, benotet, 2.5 ECTS
- Anteil an der Berechnung der Modulnote: 100.0 %
- Prüfungssprache: Englisch
- Erstablegung: WS 2021/2022, 1. Wdh.: SS 2022
1. Prüfer: | Björn Eskofier |
- Termin: 19.02.2022, 13:30 Uhr, Ort: K 1 TechF
Termin: 06.08.2022, 12:30 Uhr, Ort: StudOn Exam
Termin: 06.08.2022, 12:30 Uhr, Ort: StudOn Exam
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