Universität Wien
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053612 VU Optimisation Methods for Data Science (2023W)

4.00 ECTS (3.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

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).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Monday 02.10. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Thursday 05.10. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 09.10. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Thursday 12.10. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 16.10. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Thursday 19.10. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 23.10. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 30.10. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 06.11. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Thursday 09.11. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 13.11. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Thursday 16.11. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 20.11. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Thursday 23.11. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 27.11. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Thursday 30.11. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 04.12. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Thursday 07.12. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 11.12. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Thursday 14.12. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 08.01. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Thursday 11.01. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 15.01. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Thursday 18.01. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Monday 22.01. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Thursday 25.01. 11:30 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 29.01. 09:45 - 11:15 Seminarraum 7, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

The lectures will be held in presence.

This course will give an overview of modern optimization methods and their applications in data science.

Contents:
- Fundamentals of convex analysis
- First-order methods: gradient descent, subgradient method, acceleration, adaptivity, etc.
- Stochastic first-order methods: stochastic gradient descent, variance reduction.
- Higher-order methods: Newton's method, quasi-Newton method.

Assessment and permitted materials

(1) a written exam at the end of the semester (in person)
(2) 2-3 long homeworks during semester
(3) bonus points for active participation during classes

The date for the exam is January 25, 2024.

Minimum requirements and assessment criteria

Exam: 50%
Exercises: 50%

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 lectures

Reading list

1. A. Beck "First-Order Methods in Optimization".
2. Optimization for Machine Learning lecture notes by Martin Jaggi EPFL and Bernd Gärtner, ETH
https://raw.githubusercontent.com/epfml/OptML_course/master/lecture_notes/lecture-notes.pdf
3. Stephen Boyd and Lieven Vandenberghe. "Convex Optimization", https://web.stanford.edu/~boyd/cvxbook/.

Association in the course directory

Modul: OMD

Last modified: Mo 27.07.2026 10:55