623.504 (21S) Artificial Intelligence & Machine Learning

Sommersemester 2021

Anmeldefrist abgelaufen.

Erster Termin der LV
01.03.2021 14:00 - 16:00 online Off Campus
... keine weiteren Termine bekannt

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.
Lehrende/r
LV-Titel englisch
Artificial Intelligence & Machine Learning
LV-Art
Vorlesung-Kurs (prüfungsimmanente LV )
LV-Modell
Präsenzlehrveranstaltung (Online-Option )
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.
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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): 

  1. "Selected Topics in Artificial Intelligence" (626.017) for an in-depth review of reinforcement learning methods. 
  2. ”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

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 Benotungsschema

Position im Curriculum

  • Masterstudium Informatics (SKZ: 911, Version: 19W.1)
    • Fach: Vertiefung Informatik (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
  • 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)
  • 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
  • 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

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

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)