Universität Wien
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053630 SE Research Seminar (2024W)

4.00 ECTS (3.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle
Th 23.01. 13:15-16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Thursday 03.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02 (Kickoff Class)
  • Thursday 24.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 31.10. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 07.11. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 14.11. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 21.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 28.11. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 05.12. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 12.12. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 09.01. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 16.01. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 30.01. 13:15 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02

Information

Aims, contents and method of the course

This seminar aims to provide an overview of the current state-of-the-art data science research, highlighting key techniques and methods used in the field. The seminar will be divided into two segments. In the first part, attendees will learn about the data science research process by reading and critically evaluating scientific papers. The second part is dedicated to a practical approach where participants get hands-on experience with the tools
and methods used in applied data science research to get a sense of the process involved in applying these tools to real-world problems.

Goals of Part 1
By presenting a published research work, participants learn about the latest developments and challenges in fields such as machine learning, natural language processing, computer vision, mathematical aspects of deep learning, applied data science, and other related areas. In particular, students learn how to present and identify a research question, communicate results, and critically evaluate the quality and relevance of a paper.

Goals of Part 2
The research seminar’s coding portion is divided into four programming exercises posted and graded throughout the semester. The students are randomly selected to present their exercises in class, where their work is evaluated.

Assessment and permitted materials

Part I: Paper presentations
Paper pitch presentation (15%)
Final paper presentation (35%)

Part II: Coding exercises
Accounts for a total of 50% of the final grade (further subdivided by individual assignments). This portion of the grade depends on the total number of completed exercises alongside a successful presentation of one's work in front of the class.

Minimum requirements and assessment criteria

Half the achievable points are mandatory for an overall passing grade. Additionally, you are allowed to have up to two unexcused absences.

The grading scale for the course will be:
1: at least 87.5%
2: at least 75.0%
3: at least 62.5%
4: at least 50.0%

Examination topics

See above & the reading list below.

Reading list

A list of topics and the respective scientific papers will be made available via Moodle and discussed during the seminar's first session on 03.10.2024.

Association in the course directory

Last modified: We 11.09.2024 11:25