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BERGISCHE
UNIVERSITÄT
WUPPERTAL
Startseite    Anmelden    Semester:  SoSe 2020

Seminar: Selected Topics in Data Science - Einzelansicht

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Grunddaten
Veranstaltungsart Seminar Veranstaltungsnummer 201ELE203704
Veranstaltungskürzel SDS
Semester SoSe 2020 SWS 2
Erwartete Teilnehmer/-innen Max. Teilnehmer/-innen
Belegung Diese Veranstaltung ist nicht belegpflichtig!
Hyperlink https://dke.uni-wuppertal.de/de/teaching/
Weitere Links Moodle Course
Termine Gruppe: iCalendar Export für Outlook
  Tag Zeit Rhythmus Dauer Raum Lehrperson fällt aus am Max. Teilnehmer/-innen
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Di. 14:15 bis 15:45 woch 21.04.2020 bis 14.07.2020  FC Campus Freudenberg - FC.00.10 Lehrperson: Meisen
Gipp
Foltynek
Meuschke
  30
Gruppe :
 


Zugeordnete Personen
Zugeordnete Personen Zuständigkeit
Gipp, Bela, Univ.- Prof. Dr. verantwortlich
Meisen, Tobias, Univ.- Prof. Dr. verantwortlich
Meuschke, Norman begleitend
Tercan, Hasan begleitend
Studiengänge
Abschluss Studiengang Prüfungsversion Semester
Master an Universitäten Informationstechnologie 20111 -
Master an Universitäten Wirtschaftsing.Informat. 20171 -
Bachelor an Universitäten Informatik 20181 -
Master an Universitäten Informatik 20181 -
Zuordnung zu Einrichtungen
Elektrotechnik
Inhalt
Kurzkommentar

Parts or all of the course will be given via video conferencing software.

To receive updates on the organizational details, please register for the course in Moodle by April 20!

Kommentar

 

Course Content Description

Seminar participants will explore current research trends and established approaches in the Data Science field.

Participants can choose to complete either the theoretical or the practical research project track as part of this seminar.

 

For the theoretical research project, participants will pick a topic according to their own interests,
or from a pool of suggestions that will be provided.

For their topic, the participants will give an overview of the state-of-the-art relevant to that topic
in a presentation during the seminar (30 min) and a term paper (8 - 10 pages per person, ACM style)
due at the end of the seminar.

Through this process, which the lecturers supervise and guide, the participants will train their ability to:

  • find, organize, and systematically read relevant research papers;
  • analyze, compare, and contrast research approaches and findings; 
  • structure, write, and format an academic paper;
  • present their work using appropriate presentation techniques and presentation aids;
  • answer questions and discuss their work with peers.

The theoretical research project is best suited to compile a state-of-the-art review in preparation for a subsequent 
bachelor's or master's thesis in the same area.

 

For the practical research project, participants will implement a system that solves an applied real-world problem. Participants can suggest a problem or choose from suggestions that will be provided. In addition to delivering a functioning application, completing this seminar requires giving a presentation (30 min) about the project and compiling a developer documentation for the application (min 3 pages ACM style per person).

By completing the practical research track, participants will gain hands-experience with state-of-the-art methods and technologies and train their application development skills.

 

Topic suggestions for both tracks include, but are not limited to:

 

Recommender Systems

  • Literature Recommendation
  • Collaborator Recommendation

Plagiarism Detection

  • Semantic Text Analysis
  • Analysis of Nontextual Content Features
  • Machine Learning Approaches 
  • Mathematical Content Analysis

Blockchain Applications

  • Open Science
  • Trusted Timestamping
  • Confidential Information Retrieval

News Analysis

  • Semantic Analysis of News Articles
  • Dissemination of Information
  • News Framing Analysis
  • Clustering Related News

Artificial Neural Networks for Industrial Applications

  • Time Series Analysis for Soft Sensors
  • Time Series Forecasting for Predictive Quality Control
  • Image Recognition for Waste Product Classification
  • Transfer Learning for Simulation and Real-World Data

Explainability of Decision Processes in Artificial Neural Networks

  • Object Recognition in Convolutional Neural Networks
  • Visualization of Network Activity
  • Structure of Learning Representations
  • Importance of Network Areas for the Learning Task

By successfully completing the seminar, participants will achieve valuable preparation in terms of the knowledge
and the methodological skills 
required to successfully complete a bachelor’s or master’s thesis in the groups of
Prof. Meisen and Prof. Gipp.

Literatur

Introductory literature:

  • Python for Data Analysis - Data Wrangling with Pandas, NumPy, and IPython. 
    W. McKinney.

    O'Reilly Media, 2017. ISBN-13: 978-1491957660
  • Data Mining: The Textbook.
    C. Aggarwal.

    Springer, 2015. ISBN-13 978-3319141411
  • An Introduction to Information Retrieval. (free online edition: http://www-nlp.stanford.edu/IR-book/)
    C. D. Manning, P. Raghavan and H. Schütze. 
    Cambridge University Press, Cambridge, England 2009.
  • Foundations of statistical natural language processing.
    C. D. Manning and H. Schütze.
    Cambridge University Press, Cambridge, England 1999.
  • Web Information Retrieval.
    S. Ceri, A. Bozzon, M. Brambilla, E. Della Valle, P. Fraternali and S. Quarteroni.
    Springer, 2013. ISBN 3642393136.
  • Deep learning.
    Goodfellow, I., Bengio, Y., & Courville, A. (2016).
    MIT press. ISBN 9780262035613
  • Deep learning with Python.
    Francois, C. (2017). ISBN 9781617294433
  • Visualizing and understanding convolutional networks.
    M. D. Zeiler and R. Fergus - in Computer Vision – ECCV 2014
  • Domain randomization for transferring deep neural networks from simulation to the real world.
    J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel (2017)
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017, pp. 23–30.

Topic-specific literature will be provided during the first session.

Leistungsnachweis

For the theoretical research project:

  1. Presentation (30 min)
  2. Term paper (8-10 pages per person, ACM style)

For the practical research project:

  1. Developed application
  2. Presentation (30 min)
  3. Developer documentation (min 3 pages per person, ACM style)

Group work is possible for both project types.


Strukturbaum
Die Veranstaltung wurde 4 mal im Vorlesungsverzeichnis SoSe 2020 gefunden:

2007 WUSEL-Team Bergische Universität Wuppertal
Anzahl aktueller Nutzer/-innen auf : 138