623.504 (21S) Artificial Intelligence & Machine Learning
Überblick
Bedingt durch die COVID-19-Pandemie können kurzfristige Änderungen bei Lehrveranstaltungen und Prüfungen (z.B. Absage von Präsenz-Lehreveranstaltungen und Umstellung auf Online-Prüfungen) erforderlich sein.
Weitere Informationen zum Lehrbetrieb vor Ort finden Sie unter: https://www.aau.at/corona.
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 34 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- mögliche Sprache/n der Leistungserbringung Deutsch , Englisch
- LV-Beginn 01.03.2021
- eLearning zum Moodle-Kurs
Zeit und Ort
Beachten Sie bitte, dass sich aufgrund von COVID-19-Maßnahmen die derzeit angezeigten Termine noch ändern können.
Liste der Termine wird geladen...
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 (next semester):
- "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
Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.
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
- 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)
-
Wintersemester 2021/22
- 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)