Klausur oder mündliche Prüfung (100 %). Voraussetzung für die Zulassung zur Prüfung ist das Bestehen von Hausaufgaben.
**(!) Prüfungsbedingungen (EN)**
Written exam or oral examination (100 %). Students must pass written homework to be admitted to the module examination.
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**(!) Empfohlene Voraussetzungen (DE)**
Good knowledge of data structures and algorithms, stochastics and linear programming.
**(!) Empfohlene Voraussetzungen (EN)**
Good knowledge of data structures and algorithms, stochastics and linear programming.
**(!) Lernziele (DE)**
Knowledge: After successful completion of the module students know
**Knowledge:** After successful completion of the module students know
* Theoretical understanding of stochastic uncertainty models and optimization algorithms along with their limitations
Skills: After successful completion of the module, students will be able to
**Skills:** After successful completion of the module, students will be able to
* Clear conception of the assumptions underlying models of uncertainty and their algorithms
* Ability to give estimates on the approximation quality of the derived solutions
Competencies: Based on the knowledge and skills acquired in the module, students will be able to
**Competencies:** Based on the knowledge and skills acquired in the module, students will be able to
* Application of theoretical insights to support the selection of suitable algorithms in generalized scenarios and for new optimization tasks
**(!) Lernziele (EN)**
Knowledge: After successful completion of the module students know
**Knowledge:** After successful completion of the module students know
* Theoretical understanding of stochastic uncertainty models and optimization algorithms along with their limitations
Skills: After successful completion of the module, students will be able to
**Skills:** After successful completion of the module, students will be able to
* Clear conception of the assumptions underlying models of uncertainty and their algorithms
* Ability to give estimates on the approximation quality of the derived solutions
Competencies: Based on the knowledge and skills acquired in the module, students will be able to
**Competencies:** Based on the knowledge and skills acquired in the module, students will be able to
* Application of theoretical insights to support the selection of suitable algorithms in generalized scenarios and for new optimization tasks
**(!) Inhalt (DE)**
Optimization problems with uncertainty about the input arise in many areas of modern computing systems. Often probabilistisc assumptions about (parts of) the input is available. In this course, we discuss algorithmic techniques that can be used for such scenarios and come with provable performance guarantees. Topics include, e.g., stochastic online optimization, elementary stopping problems and combinatorial extensions, Markov decision processes, probing problems, online convex optimization and multi-armed bandit problems, or game-theoretic scenarios.
**(!) Inhalt (EN)**
Optimization problems with uncertainty about the input arise in many areas of modern computing systems. Often probabilistisc assumptions about (parts of) the input is available. In this course, we discuss algorithmic techniques that can be used for such scenarios and come with provable performance guarantees. Topics include, e.g., stochastic online optimization, elementary stopping problems and combinatorial extensions, Markov decision processes, probing problems, online convex optimization and multi-armed bandit problems, or game-theoretic scenarios.
