650.025 (20W) 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 Onlinelehrveranstaltung
- Semesterstunde/n 4.0
- ECTS-Anrechnungspunkte 6.0
- Anmeldungen 20 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 28.10.2020
- 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 inkl. Einsatz von eLearning-Tools
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/)
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.
Prüfungsinformationen
Geänderte Prüfungsinformationen (COVID-19 Ausnahmeregelung)
Presentations of projects are done online
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 presentation (50%) and the report (50%).
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
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Fach: Artificial Intelligence
(Pflichtfach)
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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
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2.2 Machine Learning and Deep Learning (
0.0h VC / 6.0 ECTS)
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Fach: Artificial Intelligence
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