Die Veranstaltung findet zusammen mit der 03-M-SP-2 statt.
Parametric statistical models and corresponding statistical methods will be introduced. In this, we pursue a general decision-theoretic approach, from which we will derive the different sub-areas of inferential statistics (point estimation, confidence estimation, hypothesis testing, etc). In prototypical model classes, we will characterize optimal decision rules.

+ Exercise

+ Übung

+ Übung

Homepage des KKSB und Uni-Lageplan

Die Veranstaltuing findet im KKSB statt.

Profil: KIKR
Schwerpunkt: IMAP-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-aml.pdf

Die Veranstaltung findet im KKSB statt.

Schwerpunkt: IMK-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-amai.pdf

Schwerpunkt: IMVT-AI

Many every-day processes can seen as a game between autonomous interacting players, where each player acts strategically in order to pursue her own objectives. This seminar covers various game-theoretic concepts and techniques, mainly with connections to applications. Use-cases are distributed systems, auctions, online-markets, resource allocation, and traffic networks.

Profil: SQ
Schwerpunkt: IMA-SQ, IMA-AI
https://lvb.informatik.uni-bremen.de/imat/03-imat-au.pdf


A key assumption of many powerful optimization methods is that all the data is fully accessible from the beginning.

However, from the point of view of many real-world applications (e.g., in logistics, production or project planning, cloud computing, etc.) this assumption is simply not true. Large data centers allocate resources to tasks without knowledge of exact execution times or energy requirements; transit times in networks are often uncertain; or, parameters such as bandwidth, demands or energy consumption are highly fluctuating. The current trend of data collection and data-driven applications often amplifies this phenomenon. As the amount of available data is increasing tremendously due to internet technology, cloud systems and sharing markets, modern algorithms are expected to be highly adaptive and learn and benefit from the dynamically changing mass of data.

In the above examples, our knowledge of the current data is only partial or based on historical estimates. The class ``Algorithms and Uncertainty'' will teach students about the most common models of such uncertain data and how to design and analyze efficient algorithms in these models.

Specifically, we will cover the theory of online optimization, where the input arrives without any prior information (such as network packets arriving to a router) and also needs to be processed immediately, before the next piece of input arrives. This model is best suited for analyzing critical networking and scheduling systems where devices and algorithms must perform well even in the worst-case scenario.

In the cases where previous history can be used to model the upcoming data, we often employ robust optimization or stochastic optimization. In robust optimization, the aim is to optimize the worst-case of all possible realizations of the input data. Hence, this model is rather conservative.
In stochastic optimization however, the algorithms work with the assumption that data is drawn from some probability distribution known ahead of time and typically the goal is to optimize the expected value.

Nowadays, another source of information is often available: machine learning algorithms can generate predictions which are accurate most of the time. However, there is no guarantee on the quality of the prediction, as the current instance may not be covered by the training set. This statement motivated a very recent research domain that will be covered in this course: how to use error-prone predictions in order to improve guaranteed algorithms.

Organization: The course will be taught in English in two sessions per week (4 SWS) including interactive exercise sessions.

Examination: The examination will be by individual oral exam. As admission to the oral exam it is mandatory to present solutions in the exercise session at least twice during the term.

Prerequisites: Having heard an introductory course to discrete algorithms and their mathematical analysis (e.g. Algorithmentheorie, Algorithmische Diskrete Mathematik) or graph theory is beneficial but not required.

Die Veranstaltung findet zusammen mit der 03-M-SP-2 statt.
Parametric statistical models and corresponding statistical methods will be introduced. In this, we pursue a general decision-theoretic approach, from which we will derive the different sub-areas of inferential statistics (point estimation, confidence estimation, hypothesis testing, etc). In prototypical model classes, we will characterize optimal decision rules.

Die Veranstaltung findet zusammen mit 03-M-FTH-10 statt.

Homepage des KKSB und Uni-Lageplan

In this course Robust control is introduced. Robust control is an approach to design controllers to deal with uncertainty. The aim is to achieve robust performance (or stability) in the presence of bounded modelling errors.

https://lvb.informatik.uni-bremen.de/ims/03-ims-apks.pdf
Termine:
Auftakttermin: 14.10.2025 um 12:00 Uhr in Raum 2.43 Rotunde Cartesium.

The course is dedicated to the numerical solution of partial differential equations. The focus is on finite differences and finite element methods.

Partial differential equations are a main component in the modelling of physical, chemical or biological phenomena in several spatial dimensions or in space and time. They also often occur in mathematical problems in geometry or calculus of variations. The lecture deals with the discretisation of partial differential equations and the estimation of the error between continuous and discrete solution. In particular, the finite difference and the finite element method are introduced and investigated. We first discuss the application of the methods to stationary elliptic problems. Later also time-dependent problems are considered. The connection of theory, numerical analysis and implementation is particularly important.

Finding a route that minimizes the travel time from Bremen to another city is easy if all information is available upfront. In reality, when starting the trip we have to decide on a mode of transport and the first parts of our route while information about congestion, and road or train track blockages become available over time. In online optimization, we address such problems where input information is revealed step by step while some parts of the solution are already fixed.

https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ra.pdf

https://lvb.informatik.uni-bremen.de/ibg/03-ibga-fi-rdl.pdf

The quantitative assessment and the management of (extreme) risks are key tools for policy makers and stakeholders in many areas such as climate and environmental research, economics, or finance and insurance. In this course, we will get familiar with basic mathematical concepts of (quantitative) risk assessment and management.

Lecture under the specialization
1) Statistics/Stochastics
2) Data Analysis

Die Veranstaltuing findet im KKSB statt.