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053640 SE Master's Seminar (2023W)
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 We 13.09.2023 09:00 to We 20.09.2023 09:00
- Deregistration possible until Sa 14.10.2023 23:59
Details
max. 25 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
takes place on Mon, Oct 2, 2023, 15:00; Sensengasse 6, second floor (please ring Prof. Moeller)
- Monday 02.10. 15:00 - 16:00 Ort in u:find Details
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 Seminar40% 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 paperTo 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 Seminar40% 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 paperTo 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: Th 09.11.2023 13:27