053640 SE Master's Seminar (2024S)
Continuous assessment of course work
Labels
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).
- Registration is open from Mo 12.02.2024 09:00 to Th 22.02.2024 09:00
- Deregistration possible until Th 14.03.2024 23:59
Details
max. 25 participants
Language: English
Lecturers
- Torsten Möller
- Timo Klein
- Fatih Kocatürk
- Nils Morten Kriege
- Lukas Miklautz
- Claudia Plant
- Katerina Schindlerova
- Sebastian Tschiatschek
Classes (iCal) - next class is marked with N
Monday
04.03.
16:45 - 18:15
Hörsaal 3, Währinger Straße 29 3.OG
(Kickoff Class)
Monday
15.04.
08:00 - 11:15
Seminarraum 10, Währinger Straße 29 2.OG
Tuesday
16.04.
13:15 - 16:30
Seminarraum 2, Währinger Straße 29 1.UG
Information
Aims, contents and method of the course
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.
Assessment and permitted materials
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
Minimum requirements and assessment criteria
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%
Examination topics
The goal is to make progress in your master thesis. You will be judged by the quality of the milestones (as mentioned above).
Reading list
Literature and further details are announced by the supervisor in the course.
Association in the course directory
Last modified: Tu 26.03.2024 11:25