053611 VU Mathematics of Data Science (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
The first lecture takes place on October 13 2022
- Tuesday 04.10. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 06.10. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 11.10. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 13.10. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 18.10. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 20.10. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 25.10. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 27.10. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 03.11. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 08.11. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 10.11. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 15.11. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 17.11. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 22.11. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 24.11. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 29.11. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 01.12. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 06.12. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 13.12. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 15.12. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 10.01. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 12.01. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 17.01. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 19.01. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 24.01. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 26.01. 10:30 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 31.01. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
Information
Aims, contents and method of the course
This course establishes a mathematical basis required to understand tools and methods in data science. Since it is expected that the students in this course come from a broad range of academical backgrounds, the classes will be adapted to the prior knowledge of the students.In this course we will get to know the following topics in various degrees of depth: high-dimensionality and dimension reduction, principle components analysis, graphs and clustering, image and signal processing, sparsity and compressed sensing
Assessment and permitted materials
Oral exam at the end of the semester.
Minimum requirements and assessment criteria
Basic knowledge of all mathematical concepts presented in the lecture.
Examination topics
Everything covered in the lectures.
Reading list
- Bishop: Pattern Recognition and Machine Learning
- Bandeira, Singer, Strohmer: Mathematics of Data Science, https://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf
- Brunton, Kutz: Data-Driven Science and Engineering
- Shalev-Shwartz, Ben-David: Understanding Machine Learning
- Bandeira, Singer, Strohmer: Mathematics of Data Science, https://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf
- Brunton, Kutz: Data-Driven Science and Engineering
- Shalev-Shwartz, Ben-David: Understanding Machine Learning
Association in the course directory
Modul: MDS
Last modified: Mo 17.10.2022 12:09