700.325 (22W) Practical Introduction to Neural Networks and Deep Learning
Überblick
Weitere Informationen zum Lehrbetrieb vor Ort finden Sie unter: https://www.aau.at/corona.
- Lehrende/r
- LV-Titel englisch Practical Introduction to Neural Networks and Deep Learning
- LV-Art Kurs (prüfungsimmanente LV )
- LV-Modell Onlinelehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 3.0
- Anmeldungen 15 (15 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 21.10.2022
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
Neural networks and deep learning (DL) 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.
Inhalt/e
- Data preprocessing / data augmentation
- Unsupervised Learning and Clustering
- Deep Learning (multilayer perceptron, convolutional models, recurrent models)
- Deep learning libraries (keras, tensorflow...etc.)
- Time series forecast
- Evaluation Metrics
Erwartete Vorkenntnisse
Python:
Good understanding about programming in python:
- Classes and objects
- Inheritance
- Context managers
- Dictionaries/lists/buffers
- Basic functional programming concepts (lambdas, map, reduce and filter)
- Basic array manipulation
Linear algebra topics related to matrix manipulation (Basics)
Probability theory basics
- Basic understanding of the concepts in probability theory such as,
- Probability density/mass functions and their associated distributions
- Conditional probability
Prüfungsinformationen
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- 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 KS / 3.0 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: 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 KS / 3.0 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: 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 KS / 3.0 ECTS)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
-
Fach: Autonomous Systems and Robotics: Advanced (ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Fach: Information and Communicatons Enginnering: Supplements
(Wahlfach)
-
1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) (
0.0h VC, KS / 14.0 ECTS)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h KS / 3.0 ECTS)
-
1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: Information and Communicatons Enginnering: Supplements
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Fach: ICE- Supplements
(Wahlfach)
-
2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) (
0.0h VC, KS / 14.0 ECTS)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h KS / 3.0 ECTS)
-
2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: ICE- Supplements
(Wahlfach)
- Bachelorstudium Robotics and Artificial Intelligence
(SKZ: 295, Version: 22W.1)
-
Fach: Robotics & AI Applications
(Wahlfach)
-
8.1 Robotics & AI Applications (
0.0h VO, VC, UE, KS / 12.0 ECTS)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h KS / 3.0 ECTS)
-
8.1 Robotics & AI Applications (
0.0h VO, VC, UE, KS / 12.0 ECTS)
-
Fach: Robotics & AI Applications
(Wahlfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
-
Wintersemester 2023/24
- 700.325 KS Lab: Neural Networks and Deep Learning (2.0h / 4.0ECTS)
-
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)
-
Wintersemester 2019/20
- 700.325 VC Practical Introduction to Neural Networks and Deep Learning (2.0h / 4.0ECTS)