053640 SE Master's Seminar (2024S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 12.02.2024 09:00 bis Do 22.02.2024 09:00
- Abmeldung bis Do 14.03.2024 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
- Torsten Möller
- Timo Klein
- Fatih Kocatürk
- Nils Morten Kriege
- Lukas Miklautz
- Claudia Plant
- Katerina Schindlerova
- Sebastian Tschiatschek
Termine (iCal) - nächster Termin ist mit N markiert
- Montag 04.03. 16:45 - 18:15 Hörsaal 3, Währinger Straße 29 3.OG (Vorbesprechung)
- Montag 15.04. 08:00 - 11:15 Seminarraum 10, Währinger Straße 29 2.OG
- Dienstag 16.04. 13:15 - 16:30 Seminarraum 2, Währinger Straße 29 1.UG
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
The aim is to conduct a data-driven project in the field of Data Science. Based on the experience gained during the implementation of the project students should learn to carry out Data Science projects on their own. The aim is also to combine previously acquired knowledge from the various courses during the study.
Art der Leistungskontrolle und erlaubte Hilfsmittel
There are four parts that contribute to the final grade in this course:
(A) a pre-paper talk
(B) an expose for your thesis project
(C) a literature review for your thesis project
(D) the attendance of talks during the semester
(A) a pre-paper talk
(B) an expose for your thesis project
(C) a literature review for your thesis project
(D) the attendance of talks during the semester
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 SeminarThe 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%
- 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 SeminarThe 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%
Prüfungsstoff
The goal is to make progress in your master thesis. You will be judged by the quality of the milestones (as mentioned above).
Literatur
Literature and further details are announced by the supervisor in the course.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Di 26.03.2024 11:25