700.325 (19W) Practical Introduction to Neural Networks and Deep Learning
Überblick
- Lehrende/r
- LV-Titel englisch Practical Introduction to Neural Networks and Deep Learning
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 24 (25 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- mögliche Sprache/n der Leistungserbringung Deutsch
- LV-Beginn 07.01.2020
- eLearning zum Moodle-Kurs
- Studienberechtigungsprüfung Ja
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
Neural networks and deep learning have different applications in text categorization, e.g., spam filtering, fraud detection, optical character recognition, machine vision, e.g., face detection, licenses plate recognition, advanced driver assistance systems, natural-language processing, e.g., spoken language understanding, market segmentation, e.g., predict if a customer will get a credit, and bioinformatics, e.g., classify proteins or lipidomes according to their function.
The lecture will cover the practical topics regarding (a) Neural networks and deep learning models, (b) guide to transfer the acquired knowledge to solve classification problems for industry and research, and (c) show some use-cases and interesting applications from the state-of-the-art.
Lehrmethodik inkl. Einsatz von eLearning-Tools
Theory + practical examples (Python)
Inhalt/e
- Data preprocessing
- Unsupervised Learning and Clustering
- Deep Learning (multilayer perceptron, convolutional models, recurrent models)
- Deep learning libraries (torch, theano, keras, tensorflow...etc.)
- Time series forecast
- Evaluation Metrics
Prüfungsinformationen
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Bachelorstudium Informationstechnik
(SKZ: 289, Version: 17W.1)
-
Fach: Informationstechnische Vertiefung
(Wahlfach)
-
10a.3 Wahl von Lehrveranstaltungen (
0.0h VO/VC/KS/UE / 6.0 ECTS)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h VC / 4.0 ECTS)
-
10a.3 Wahl von Lehrveranstaltungen (
0.0h VO/VC/KS/UE / 6.0 ECTS)
-
Fach: Informationstechnische Vertiefung
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Autonomous Systems and Robotics: Advanced (ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
-
Fach: Autonomous Systems and Robotics: Advanced (ASR)
(Wahlfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
-
Wintersemester 2023/24
- 700.325 KS Lab: Neural Networks and Deep Learning (2.0h / 4.0ECTS)
-
Wintersemester 2022/23
- 700.325 KS Practical Introduction to Neural Networks and Deep Learning (2.0h / 3.0ECTS)
-
Wintersemester 2021/22
- 700.325 VC Practical Introduction to Neural Networks and Deep Learning (2.0h / 4.0ECTS)
-
Wintersemester 2020/21
- 700.325 VC Practical Introduction to Neural Networks and Deep Learning (2.0h / 4.0ECTS)