053640 SE Master's Seminar (2024W)
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 Fr 13.09.2024 09:00 to Fr 20.09.2024 09:00
- Deregistration possible until Mo 14.10.2024 23:59
Details
max. 25 participants
Language: English
Lecturers
- Torsten Möller
- Tara Andrews
- Anna Beer
- Hrvoje Bogunovic
- Tatyana Krivobokova
- Thierry Langer
- Sebastian Tschiatschek
- Yllka Velaj
- Edgar Weippl
- Jürgen Zanghellini
Classes (iCal) - next class is marked with N
- Thursday 03.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- N Wednesday 20.11. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
- Friday 22.11. 08:00 - 09:30 Seminarraum 5, Währinger Straße 29 1.UG
Information
Aims, contents and method of the course
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.
Assessment and permitted materials
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
Minimum requirements and assessment criteria
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.
Examination topics
The goal is to make progress in your master thesis. You will be judged by the milestones you and your supervisor will agree upon.
Reading list
Literature and further details are announced by the supervisor in the course.
Association in the course directory
Last modified: We 23.10.2024 12:05