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053640 SE Master's Seminar (2022W)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mi 14.09.2022 09:00 bis Mi 21.09.2022 09:00
- Abmeldung bis Fr 14.10.2022 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Montag 03.10. 15:00 - 16:30 Seminarraum 8, Währinger Straße 29 1.OG
- Donnerstag 19.01. 08:00 - 11:15 Seminarraum 18 Kolingasse 14-16, OG02
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 (or lit review) about your thesis topic
3. having a final presentation
1. doing a "pre-paper" talk
2. submitting an expose (or lit review) about your thesis topic
3. having a final presentation
Mindestanforderungen und Beurteilungsmaßstab
Prerequisites for the Masterseminar are the successful completion of:
- 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 Seminar50% of the grade: quality of the survey paper / thesis proposal
25% of the grade: quality of the pre-paper talk
25% of the grade: quality of the final presentationIn order to pass the course, you need to achieve at least half of the points for the paper and the presentation, each.
- 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 Seminar50% of the grade: quality of the survey paper / thesis proposal
25% of the grade: quality of the pre-paper talk
25% of the grade: quality of the final presentationIn order to pass the course, you need to achieve at least half of the points for the paper and the presentation, each.
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 12.12.2022 15:28