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053640 SE Master's Seminar (2022W)
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 14.09.2022 09:00 to We 21.09.2022 09:00
- Deregistration possible until Fr 14.10.2022 23:59
Details
max. 25 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Monday 03.10. 15:00 - 16:30 Seminarraum 8, Währinger Straße 29 1.OG
- Thursday 19.01. 08:00 - 11:15 Seminarraum 18 Kolingasse 14-16, OG02
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 (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
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 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.
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: Mo 12.12.2022 15:28