Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.
053640 SE Master's Seminar (2024W)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Fr 13.09.2024 09:00 bis Fr 20.09.2024 09:00
- Abmeldung bis Mo 14.10.2024 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
- Torsten Möller
- Tara Andrews
- Anna Beer
- Hrvoje Bogunovic
- Tatyana Krivobokova
- Thierry Langer
- Sebastian Tschiatschek
- Yllka Velaj
- Edgar Weippl
- Jürgen Zanghellini
Termine (iCal) - nächster Termin ist mit N markiert
- Donnerstag 03.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Mittwoch 20.11. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
- Freitag 22.11. 08:00 - 09:30 Seminarraum 5, Währinger Straße 29 1.UG
- N Montag 27.01. 09:45 - 11:15 Seminarraum 5, Währinger Straße 29 1.UG
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
The aim of the course is to prepare you for your thesis. You are supposed to present your thesis topic to your peers to get early feedback and to become aware of related work / what others are doing.
Art der Leistungskontrolle und erlaubte Hilfsmittel
There are three steps toward the overall goal:
1. doing a "pre-paper" talk
2. submitting an expose on your thesis topic
3. submitting a literature review on your thesis topic
1. doing a "pre-paper" talk
2. submitting an expose on your thesis topic
3. submitting a literature review on your thesis topic
Mindestanforderungen und Beurteilungsmaßstab
Prerequisites for the Masterseminar are the successful completion of the following:
- Introduction to Machine Learning
- Statistics for Data Science
- Mathematics for Data Science
- Optimization methods for Data Science
- Mining Massive Data
- Visual and Exploratory Analysis
- Doing Data Science
- Ethical and Legal Issues
- Data Analysis Project and Seminar30% of the grade: quality of the thesis proposal
30% of the grade: quality of the pre-paper talk
30% of the grade: quality of the survey paper
10% of the grade: participationTo pass the course, you need to achieve at least half of the points each for the paper and the presentation.
- Introduction to Machine Learning
- Statistics for Data Science
- Mathematics for Data Science
- Optimization methods for Data Science
- Mining Massive Data
- Visual and Exploratory Analysis
- Doing Data Science
- Ethical and Legal Issues
- Data Analysis Project and Seminar30% of the grade: quality of the thesis proposal
30% of the grade: quality of the pre-paper talk
30% of the grade: quality of the survey paper
10% of the grade: participationTo pass the course, you need to achieve at least half of the points each for the paper and the presentation.
Prüfungsstoff
The goal is to make progress in your master thesis. You will be judged by the milestones you and your supervisor will agree upon.
Literatur
Literature and further details are announced by the supervisor in the course.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mo 13.01.2025 13:25