650.025 (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 Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 4.0
- ECTS-Anrechnungspunkte 6.0
- Anmeldungen 35 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 13.10.2021
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
The course provides a practical introduction into machine learning methods with the focus on deep learning.
Lehrmethodik
Lectures with practical sessions and a student's project applying machine learning to a practical problem.
Inhalt/e
- Introduction to AI and machine learning
- Machine learning preliminaries
- Basic ML approaches
- Artificial Neural Networks
- Deep Learning Architectures
- Applications
Erwartete Vorkenntnisse
The course assumes the basic prior knowledge of the probability theory, linear algebra, and optimization methods. Knowledge of Python programming language is a plus.
Curriculare Anmeldevoraussetzungen
No prerequisites
Literatur
Course book:
- Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning Cambridge: MIT press. (Available online: https://www.deeplearningbook.org/)
- Aston Zhang A., Lipton, Z.C., Li M., & Smola A.J. Dive into Deep Learning (2020) (Available online: https://d2l.ai/)
Extra literature for beginners:
- James, G., Witten, D., & Hastie, T. (2014). An Introduction to Statistical Learning: With Applications in R. Springer
- Raschka, S. (2015). Python machine learning. Packt Publishing Ltd.
Extra literature - classics:
- Mitchell, T. (1997) Machine Learning. McGraw Hill. (a bit old, but still the best intro to ML for computer scientists)
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
- Friedman, J., Hastie, T., & Tibshirani, R. (2009). The elements of statistical learning. 2nd edition, Springer.
Please consider visiting also the practical course 623.625 "Machine Learning and Deep Learning" of Pierre Tassel, which provides an introduction to various aspects of programming deep neural networks with PyTorch.
Prüfungsinformationen
Prüfungsmethode/n
Grades are given based on a project:
- implement a practical project applying machine learning techniques presented in the course
- the project is to be accomplished by a group of student comprising max. 2 participants
- each group prepares only one final presentation of their project, during which every student must be ready to answer any question regarding the presented work
- expected and accomplished tasks of every student in the project should be clearly indicated in both project proposal and report, respectively - each group participant should submit a separate report describing his/her work
- every student must accomplish at least one task which is clearly related to machine learning
Prüfungsinhalt/e
Theoretical and practical aspects of techniques used in the project report and the presentation.
Beurteilungskriterien/-maßstäbe
Grades are given based on the project proposal (20%), presentation (30%), and project report (50%).
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Informatics
(SKZ: 911, Version: 19W.2)
-
Fach: Artificial Intelligence
(Wahlfach)
-
Weitere LVen aus dem gewählten Spezialisierungsfach (
0.0h XX / 12.0 ECTS)
- 650.025 Machine Learning and Deep Learning (4.0h VC / 6.0 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: Artificial Intelligence
(Pflichtfach)
-
2.2 Machine Learning and Deep Learning (
0.0h VC / 6.0 ECTS)
- 650.025 Machine Learning and Deep Learning (4.0h VC / 6.0 ECTS) Absolvierung im 1. Semester empfohlen
-
2.2 Machine Learning and Deep Learning (
0.0h VC / 6.0 ECTS)
-
Fach: Artificial Intelligence
(Pflichtfach)