623.625 (21W) Machine Learning and Deep Learning
Überblick
Weitere Informationen zum Lehrbetrieb vor Ort finden Sie unter: https://www.aau.at/corona.
- Lehrende/r
- LV-Titel englisch Machine Learning and Deep Learning
- LV-Art Praktikum (prüfungsimmanente LV )
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 29 (15 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 08.11.2021
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
The course offers an introduction to practical deep learning.
This includes teaching you important concepts about deep learning and how to implement them using Pytorch framework.
Through this course, you will learn theoretical concepts and apply them to solve real practical problems.
Lehrmethodik
Theoretical concepts about deep learning will be explained and discussed along with how to implement them.
Solution proposed by students for each homework will be presented and discussed.
As deep learning is an empirical science , there will be plenty of room for discussion of your observations.
Inhalt/e
- Linear Algebra/Calculus/Probability
- Linear Regression
- Multi Layers Perceptrons
- Convolutional Neural Network
- Recurrent Neural Networks
- Computer Visions
- Natural Language Processing
Erwartete Vorkenntnisse
Python 3
Prüfungsinformationen
Prüfungsmethode/n
During the semester, multiple at-home mini-projects will be given to the students. One student will then be randomly selected to present his solution to the classroom, following a discussion session.
Grades will be composed of the quality of the solutions proposed.
Prüfungsinhalt/e
Deep Learning with Pytorch.
Propose a Deep Learning solution to a Kaggle problem.
Beurteilungskriterien/-maßstäbe
At home + oral presentation
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Informatics
(SKZ: 911, Version: 19W.2)
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Fach: Artificial Intelligence
(Wahlfach)
-
Weitere LVen aus dem gewählten Spezialisierungsfach (
0.0h XX / 12.0 ECTS)
- 623.625 Machine Learning and Deep Learning (2.0h PR / 4.0 ECTS) Absolvierung im 1., 2. Semester empfohlen
-
Weitere LVen aus dem gewählten Spezialisierungsfach (
0.0h XX / 12.0 ECTS)
-
Fach: Artificial Intelligence
(Wahlfach)
- Masterstudium Artificial Intelligence and Cybersecurity
(SKZ: 993, Version: 20W.1)
-
Fach: Specialisation in Artificial Intelligence and Cybersecurity
(Wahlfach)
-
Fachlich relevante Lehrveranstaltungen (
0.0h XX / 34.0 ECTS)
- 623.625 Machine Learning and Deep Learning (2.0h PR / 4.0 ECTS) Absolvierung im 2., 3. Semester empfohlen
-
Fachlich relevante Lehrveranstaltungen (
0.0h XX / 34.0 ECTS)
-
Fach: Specialisation in Artificial Intelligence and Cybersecurity
(Wahlfach)