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261-5110-00L 10 Credits MSC , WBZ D-ITET , D-INFK , D-MATH , D-ARCH
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Optimization for Data Science

Lecturers & Examiners: Prof. Dr. Bernd Gärtner, Prof. Dr. Niao He
VVZ CR 1.7

Last Updated: 2026-02-05 16:38:54

Abstract

This course provides an in-depth theoretical treatment of optimization methods that are relevant in data science.

Objective

Understanding the guarantees and limits of relevant optimization methods used in data science. Learning theoretical paradigms and techniques to deal with optimization problems arising in data science.

Content

This course provides an in-depth theoretical treatment of classical and modern optimization methods that are relevant in data science. After a general discussion about the role that optimization has in the process of learning from data, we give an introduction to the theory of (convex) optimization. Based on this, we present and analyze algorithms in the following four categories: first-order methods (gradient and coordinate descent, Frank-Wolfe, subgradient and mirror descent, stochastic and incremental gradient methods); second-order methods (Newton and quasi Newton methods); non-convexity (local convergence, provable global convergence, cone programming, convex relaxations); min-max optimization (extragradient methods). The emphasis is on the motivations and design principles behind the algorithms, on provable performance bounds, and on the mathematical tools and techniques to prove them. The goal is to equip students with a fundamental understanding about why optimization algorithms work, and what their limits are. This understanding will be of help in selecting suitable algorithms in a given application, but providing concrete practical guidance is not our focus.

Resources

Learning Materials (Links)

General Information

Language
English
Levels
MSC , WBZ
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 180 minutes
Aids
4 pages (A4) of written material (no restrictions regarding form or content)
At two times during the course of the semester, we will hand out graded assignments (compulsory continuous performance assessments). The solutions are expected to be typeset in LaTeX or similar. Solutions will be graded and contribute 30% to the final grade. Concretely, let P1 and P2 be the performances in the two graded assignments, measured as the percentage of points being attained (between 0% and 100%). A graded assignment that is not handed in is counted with a performance of 0%. Let PE be the performance in the final exam. Then the overall course performance is computed as P = 0.15*P1 + 0.15*P2 + 0.7*PE. A course performance of P >= 50% is guaranteed to lead to a passing grade, but depending on the overall performance of the cohort, we may lower the threshold for a passing grade. Assignments can be discussed with colleagues, but we expect an independent writeup.

Course Components

Type Title Time & Place Hours
lecture Optimization for Data Science
  • Mon 13:15-14:00 (NO C 60)
  • Tue 10:15-12:00 (ETF C 1)
3 h weekly
exercise Optimization for Data Science
  • Tue 14:15-16:00 (CAB G 51)
  • Fri 14:15-16:00 (ML H 44)
2 h weekly
independent project Optimization for Data Science No time listed 4 h weekly

Offered In