Project Representation Learning (PRL)10 ECTS
(englische Bezeichnung: Project Representation Learning)
(Prüfungsordnungsmodul: Project Representation Learning)

Lehrveranstaltungen:

• • Project Representation Learning
    (Sonstige Lehrveranstaltung, Bernhard Kainz)

Empfohlene Voraussetzungen:

recommended:
Deep Learning ML Prof. Dr. Andreas Maier 2+2 5 x E
Pattern Recognition ML Prof. Dr. Andreas Maier 3+1+2 5 x E
Maschinelles Lernen für Zeitreihen ML Prof. Eskofier, Prof. Oliver Amft, Dr. Ch. Mutschler 2+2+2 7.5 x E

Es wird empfohlen, folgende Module zu absolvieren, bevor dieses Modul belegt wird:

Deep Learning (SS 2021)
Pattern Recognition (WS 2020/2021)
Maschinelles Lernen für Zeitreihen (WS 2020/2021)

Inhalt:

Different projects in the area of (deep) representation learning are on offer. These reach from theoretical exploration of new data representation methods to practical evaluation of applications in, e.g., medical image analysis.
Example projects will be made available on the website of the chair for health data science. Students may also propose their own projects, which will be coordinated and refined with the module lead during preliminary discussions.

Lernziele und Kompetenzen:

Considerably more in-depth knowledge of deep representation learning, including deeper insight into current research and development work.
A capability to contribute to research and development work.
To use a holistic view to identify, formulate and deal with complex issues critically, independently and creatively.
To follow a scientific approach, formulating hypotheses, validation through experimentation and statistical analysis.
To plan and use adequate methods to conduct qualified tasks in given frameworks and to evaluate this work.
To create, analyse and critically evaluate different technical/architectural solutions.
To integrate knowledge critically and systematically.
To clearly present and discuss the conclusions as well as the knowledge and arguments that form the basis for these findings in written and spoken English.
To identify the issues that must be addressed within the framework of the specific thesis in order to take into consideration all relevant dimensions of sustainable development.
A consciousness of the ethical aspects of research and development work.

Literatur:

A specific reading list will be established at the beginning of each project, general literature is listed below:
Quinn J, McEachen J, Fullan M, Gardner M, Drummy M. Dive into deep learning: Tools for engagement. Corwin Press; 2019 Jul 15. https://d2l.ai/
Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Cambridge: MIT press; 2016 Nov 18. https://www.deeplearningbook.org/

Studien-/Prüfungsleistungen:

Project Representation Learning (Prüfungsnummer: 31121)

Prüfungsleistung, Praktikumsleistung, benotet, 10 ECTS

Anteil an der Berechnung der Modulnote: 100.0 %

weitere Erläuterungen:
Written report and oral presentation: a) Top achievement:

• Knowledge: Solid knowledge, insight and overview.

• Application: Solid exposition, independent application and

critical reflection b) Medium achievement:

• Knowledge: Some knowledge and insight

• Application: Clear exposition and relative consistent

application c) Achievement for passing:

• Knowledge: Sufficient, but limited knowledge

• Application: Sufficient exposition and application
  

Prüfungssprache: Deutsch und Englisch

Erstablegung: SS 2022, 1. Wdh.: WS 2022/2023

1. Prüfer:

Bernhard Kainz

Ort: zoom
Ort: zoom