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053612 VU Optimisation Methods for Data Science (2022W)
Continuous assessment of course work
Labels
ON-SITE
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
Written Exam:
Thursday 19 Jan 2023, 11:30-13:00, location: HS 11 OMP- Monday 03.10. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 06.10. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 10.10. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 13.10. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 17.10. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 20.10. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 24.10. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 27.10. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 31.10. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 03.11. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 07.11. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 10.11. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 14.11. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 17.11. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 21.11. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 24.11. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 28.11. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 01.12. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 05.12. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 12.12. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 15.12. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 09.01. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 12.01. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 16.01. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
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Thursday
19.01.
11:30 - 13:00
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock - Monday 23.01. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 26.01. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Monday 30.01. 09:45 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
Information
Aims, contents and method of the course
The lecture will be held in presence.This course will cover foundations of duality, a key technology in optimization, with a special focus on (larger scale) algorithmic applications in data science.
Assessment and permitted materials
(1) a written exam at the end of the semester
(2) weekly exercises
(3) bonus points for active participation during classesWritten Exam:Thursday 19 Jan 2023, 11:30-13:00, location: HS 11 OMP
(2) weekly exercises
(3) bonus points for active participation during classesWritten Exam:Thursday 19 Jan 2023, 11:30-13:00, location: HS 11 OMP
Minimum requirements and assessment criteria
Exam: 50%
Exercises: 35%
Activity: 15%Precentage/Grades:0-53: nicht genuegend/fail (5)
54-65: genuegend/pass (4)
66-77: befriedigend/satisfactory (3)
78-89: gut/good (2)
90-100: sehr gut/excellent (1)
Exercises: 35%
Activity: 15%Precentage/Grades:0-53: nicht genuegend/fail (5)
54-65: genuegend/pass (4)
66-77: befriedigend/satisfactory (3)
78-89: gut/good (2)
90-100: sehr gut/excellent (1)
Examination topics
all material covered during the lecture
Reading list
Lecture Notes (see moodle)Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear Programming: Theory and Algorithms, WileyStephen Boyd and Lieven Vandenberghe.
Convex Optimization.
https://web.stanford.edu/~boyd/cvxbook/.Martin Jaggi, Bernd Gärtner,
Optimization for Machine Learning (Lect.notes)
https://raw.githubusercontent.com/epfml/OptML_course/master/lecture_notes/lecture-notes.pdf
Convex Optimization.
https://web.stanford.edu/~boyd/cvxbook/.Martin Jaggi, Bernd Gärtner,
Optimization for Machine Learning (Lect.notes)
https://raw.githubusercontent.com/epfml/OptML_course/master/lecture_notes/lecture-notes.pdf
Association in the course directory
Modul: OMD
Last modified: Mo 14.11.2022 15:28