623.504 (21W) Artificial Intelligence & Machine Learning
Überblick
Weitere Informationen zum Lehrbetrieb vor Ort finden Sie unter: https://www.aau.at/corona.
- Lehrende/r
- LV-Titel englisch Artificial Intelligence & Machine Learning
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 27 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 07.10.2021
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
The course provides a practical introduction into artificial intelligence methods with a focus on machine learning and it’s applications in computer science.
Please consider also visiting:
- "Machine Learning and Deep Learning" (650.025) for an in-depth overview of neural networks and their applications.
- "Selected Topics in Artificial Intelligence" (626.017) for an in-depth review of reinforcement learning methods.
- ”Current Topics in Multimedia Systems: Content Search with Deep Learning” (623.915) which among other interesting topics considers various architectures and applications of Deep Neural Networks to image/video processing and recognition.
Lehrmethodik
Lectures with a student's project applying machine learning to a practical problem.
Inhalt/e
- Introduction to AI and machine learning
- Supervised learning: classification and regression
- Unsupervised learning: transformation of data and clustering
- Validation of models
- Overview of the reinforcement learning
Erwartete Vorkenntnisse
The course has makes no assumptions about the prior knowledge, but basic knowledge of the probability theory as well as of Python is a plus.
Curriculare Anmeldevoraussetzungen
No prerequisites
Literatur
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.
Classics:
- Mitchell, T. (1997) Machine Learning. McGraw Hill.
- 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)
All presentations will be 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 proposal (20%), presentation (30%), and project report (50%).
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Informatics
(SKZ: 911, Version: 19W.2)
-
Fach: Vertiefung Informatik (Specialization in Informatics)
(Pflichtfach)
-
1.4 Artificial Intelligence & Machine Learning (
2.0h VC / 4.0 ECTS)
- 623.504 Artificial Intelligence & Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 2. Semester empfohlen
-
1.4 Artificial Intelligence & Machine Learning (
2.0h VC / 4.0 ECTS)
-
Fach: Vertiefung Informatik (Specialization in Informatics)
(Pflichtfach)
- Masterstudium Angewandte Informatik
(SKZ: 911, Version: 13W.1)
-
Fach: Freie Wahlfächer
(Freifach)
-
Freie Wahlfächer (
0.0h XX / 6.0 ECTS)
- 623.504 Artificial Intelligence & Machine Learning (2.0h VC / 4.0 ECTS)
-
Freie Wahlfächer (
0.0h XX / 6.0 ECTS)
-
Fach: Freie Wahlfächer
(Freifach)
- Masterstudium Information Management
(SKZ: 922, Version: 19W.1)
-
Fach: Information and IT Management
(Pflichtfach)
-
3.3 Current Topics in Information and IT Management (
0.0h VC, KS, SE / 4.0 ECTS)
- 623.504 Artificial Intelligence & Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 1., 2., 3. Semester empfohlen
-
3.3 Current Topics in Information and IT Management (
0.0h VC, KS, SE / 4.0 ECTS)
-
Fach: Information and IT Management
(Pflichtfach)
- Masterstudium Information Management
(SKZ: 922, Version: 19W.1)
-
Fach: Specialisation in Information Management
(Wahlfach)
-
Specialisation in Information Management (
0.0h VO, VC, KS / 16.0 ECTS)
- 623.504 Artificial Intelligence & Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 1., 2., 3. Semester empfohlen
-
Specialisation in Information Management (
0.0h VO, VC, KS / 16.0 ECTS)
-
Fach: Specialisation in Information Management
(Wahlfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
- Sommersemester 2024
-
Wintersemester 2023/24
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2023
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Wintersemester 2022/23
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2022
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2021
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Wintersemester 2020/21
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2020
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)