Modules

45 results

B Elective Modules45

Advanced ProgrammingNo ratings for this module yet.B1.1, B2.1, B3You will learn to develop scientific software in C++ while balancing performance and maintainability. In the end you will be able to choose appropriate data types and programming structures, apply C++ language facilities for resource management, object- and generic programming, and analyze and optimize performance.5 ECTSruns this semesterIN1503Business Analytics and Machine LearningNo ratings for this module yet.B1.1, B2.1, B3You learn methods of statistical analysis and machine learning for classification, numerical prediction and clustering. After the module you can explain the assumptions and workings of common procedures and analyze datasets with R as well as interpret results.5 ECTSruns this semesterIN2028Cloud Information SystemsNo ratings for this module yet.B1.1, B2.1, B3You will learn the fundamentals and central technologies of public clouds (virtualization, containers, orchestration) and how cloud-native information systems are built (Client/Server, middleware, microservices). In the end you will be able to analyze public cloud services, determine requirements and challenges in the design and operation of cloud-native systems, and design cost-efficient and scalable architectures with current cloud technologies.5 ECTSruns this semesterCIT3230002Data Mining und Knowledge DiscoveryNo ratings for this module yet.B1.1, B2.1, B3You will learn methods of Data Mining and Knowledge Discovery: from data sources and quality through preprocessing, visualization and feature selection to correlation, regression, forecasting, classification and clustering. In the end you will be able to select appropriate methods, apply them and critically evaluate them, as well as deepen the foundations independently.3 ECTSruns this semesterIN2030Discrete OptimizationNo ratings for this module yet.B1.1, B2.1, B3You will learn central concepts and algorithms of linear integer optimization and combinatorial optimization. In the module you will analyze mathematical structures that allow efficient solution methods, and you will be able to model real problems as discrete optimization problems and identify special cases that are efficiently solvable.9 ECTSruns this semesterCIT413041
40 more in B Elective ModulesFortgeschrittene Konzepte verteilter Datenbanken - Programming Database Web ApplicationsNo ratings for this module yet.B1.1, B2.1, B3You will learn practically how web applications are built and implemented: client, server, and database. In the end you will be able to independently conceptualize, plan, and implement web applications as well as assess web business models.4 ECTSruns this semesterIN2140Fundamentals of Artificial IntelligenceNo ratings for this module yet.B1.1, B2.1, B3You learn the foundations of artificial intelligence: from search procedures and constraint-satisfaction to logic and probabilistic models to decision making, learning and an introduction to robotics. In the end you will be able to design simple AI systems and apply basic methods from search, logic, probability and decision theory.6 ECTSruns this semesterIN2406Fundamentals of Mathematical StatisticsNo ratings for this module yet.B1.1, B2.1, B3You will learn the fundamental principles and methods of statistical inference, including likelihood approaches, Bayesian methods and minimax estimation. You will deal with asymptotic theory for large samples and apply it in particular to the study and application of maximum-likelihood techniques.9 ECTSruns this semesterMA5441FunktionalanalysisNo ratings for this module yet.B1.1, B2.1, B3You will learn the basics of functional analysis in Banach and Hilbert spaces. In the end you will be able to analyze linear functionals and bounded as well as compact self-adjoint operators, understand duality, and apply concepts such as weak and weak* convergence.9 ECTSruns this semesterMA3001Höhere AlgorithmikNo ratings for this module yet.B1.1, B2.1, B3You will learn fundamental techniques for the development and analysis of efficient algorithms (e.g., Divide-and-Conquer, dynamic programming, randomization, Greedy methods, amortized analysis) and apply them to central problems such as sorting, graph problems, string and sequence algorithms, as well as data structures. In the end you will be able to understand, analyze, and use classical algorithmic procedures to solve fundamental tasks.8 ECTSruns this semesterCIT323004Machine LearningNo ratings for this module yet.B1.1, B2.1, B3You will learn the probabilistic foundations of machine learning and central learning algorithms — from simple neighborhood and clustering methods to linear models and support vector machines up to neural networks and the EM method. By the end you will be able to select, describe and derive suitable algorithms for given problems.8 ECTSruns this semesterIN2064Nichtlineare OptimierungNo ratings for this module yet.B1.1, B2.1, B3You engage with theory and numerical methods of nonlinear optimization. You will learn advanced methods for unconstrained and, in particular, constrained optimization problems (e.g., SQP, barrier and interior-point methods) and you will be able to assess and apply convergence properties of such methods in the end.5 ECTSruns this semesterMA3503Probability TheoryNo ratings for this module yet.B1.1, B2.1, B3You will learn the measure-theoretic probability theory for sequences of random variables and martingales. By the end you will be able to understand and apply central results such as the law of large numbers, central limit theorems, and fundamental martingale results.9 ECTSruns this semesterMA2409Query OptimizationNo ratings for this module yet.B1.1, B2.1, B3You learn how relational database systems process queries and how to optimize them efficiently. In the end you will be able to determine optimal join orders, apply cost and cardinality models, and make optimization decisions considering physical properties such as memory access or index usage.6 ECTSruns this semesterIN2219Statistical Foundations of LearningNo ratings for this module yet.B1.1, B2.1, B3You learn statistical foundations of machine learning theory and mathematical tools for the analysis of learning algorithms. In the end you will be able to evaluate generalization and consistency questions, theoretically analyze algorithms such as k‑NN, SVM and simple neural networks, and contextualize newer developments such as overparameterization and training dynamics.8 ECTSruns this semesterCIT4230004Statistical LearningNo ratings for this module yet.B1.1, B2.1, B3You learn the basics and advanced methods of supervised and unsupervised statistical learning. In the end you will be able to understand and apply models for regression and classification, use techniques for dimensionality reduction and cluster analysis, and follow probabilistic formulations of learning problems while designing new algorithms.6 ECTSruns this semesterMA4802Approximation AlgorithmsNo ratings for this module yet.B1.1, B2.1, B3You learn how to design and analyze efficient approximation algorithms for combinatorial optimization problems. In the end you can assess the running time and approximation guarantees of algorithms, apply known techniques (e.g. Greedy, LP-Rounding, Primal-Dual), and prove limits of approximability.9 ECTSno date this semesterCIT4100003Basic Mathematical Methods for Imaging and VisualizationNo ratings for this module yet.B1.1, B2.1, B3You learn fundamental mathematical methods that are applied in imaging and visualization, and you can subsequently use them to formulate, analyze and solve concrete problems. Topics include, among others, linear algebra, analysis, optimization and probability theory and their applications in image processing and computer vision. In the end you will be able to select appropriate methods, optimize them and transfer them to related engineering disciplines.5 ECTSno date this semesterIN2124Blockchain-based Systems EngineeringNo ratings for this module yet.B1.1, B2.1, B3You learn about the structure, properties and applications of blockchain and distributed ledger technologies (DLT). By the end you will be able to analyze blockchain-based application systems, select technologies suitable for concrete use cases, and justify fundamental architecture and design decisions.5 ECTSno date this semesterIN2359Causal Inference in Time SeriesNo ratings for this module yet.B1.1, B2.1, B3You will learn methods of causal inference for time series and apply them. By the end you will be able to identify causal structures in dynamic networks, apply suitable algorithms and implement results practically on real data sets.5 ECTSno date this semesterCIT4230006CausalityNo ratings for this module yet.B1.1, B2.1, B3You engage with concepts and methods of causal inference: from probabilistic and graph-theoretic foundations through structural models to modern algorithms for causal discovery and estimation of causal effects. In the end you can judge whether and how causal conclusions are possible from given data and assumptions, select appropriate methods and apply them in practice.8 ECTSno date this semesterIN2410Cloud-Based Data ProcessingNo ratings for this module yet.B1.1, B2.1, B3You will learn how large-scale, cloud-based systems are designed, implemented and operated. The module conveys foundations of distributed systems, data center infrastructure and current cloud technologies as well as methods for scaling, resource management and identifying bottlenecks.6 ECTSno date this semesterCIT323005Computational StatisticsNo ratings for this module yet.B1.1, B2.1, B3You will learn methods of computational statistics for high-dimensional, hierarchical, and latent data structures and how to apply them. The focus is on simulation (univariate and multivariate), Bayesian inference with MCMC (Gibbs, Metropolis-Hastings, Hamiltonian MC), bootstrap procedures and the EM algorithm for missing or latent data. In the end you can theoretically understand the algorithms, implement them in R, and interpret results statistically.5 ECTSno date this semesterMA4402Cryptography for Decentralized SystemsNo ratings for this module yet.B1.1, B2.1, B3You will learn cryptographic fundamentals and advanced techniques used in decentralized systems and blockchains. The focus is on Zero-Knowledge Proofs (including SNARKs), polynomial-based methods, confidential computing (Trusted Execution Environments), and Secure Multiparty Computation. In the end you will be able to describe concepts, assess them, and apply them to solutions in decentralized systems.6 ECTSno date this semesterCIT333003Database Systems on Modern CPU ArchitecturesNo ratings for this module yet.B1.1, B2.1, B3You learn how modern CPU architectures (CPU, cache, TLB, memory hierarchy, branch prediction) influence the behavior and performance of database systems. In the end you will be able to analyze memory access patterns, adapt database internals so that they use CPU/cache efficiently, and assess cache-aware algorithms as well as compression methods for throughput improvement.6 ECTSno date this semesterIN2118Distributed SystemsNo ratings for this module yet.B1.1, B2.1, B3You will learn the core principles and building blocks of large-scale distributed systems: communication, coordination, fault tolerance, and replication. In the end you will be able to understand typical algorithms and protocols (e.g., logical clocks, consensus/Paxos, replication schemes, DHTs), assess their properties, and apply them in the design of distributed applications.5 ECTSno date this semesterIN2259Fundamentals of Optimization for Machine LearningNo ratings for this module yet.B1.1, B2.1, B3You will learn fundamentals and advanced techniques of optimization, both convex and nonconvex as well as combinatorial and continuous, with a focus on applications in machine learning. In the end you will be able to understand optimization problems from ML research and approach research questions in this area.5 ECTSno date this semesterCIT413031Generalized Linear ModelsNo ratings for this module yet.B1.1, B2.1, B3You learn methods of regression for non-normally distributed target variables (e.g., binary, count data, nominal, positive values). In addition to classical GLMs such as logistic, Probit-, Poisson-, Gamma-, and log-linear models, extensions (e.g., overdispersion, random effects) are covered. By the end you will be able to estimate models, validate them, and analyze and interpret the results with R.9 ECTSno date this semesterMA3403Graphical Models in StatisticsNo ratings for this module yet.B1.1, B2.1, B3In this module you will learn statistical models whose conditional independencies are described by graphs. The focus is on continuous distributions (in particular the multivariate normal distribution), undirected graphical models (Gaussian models) and directed acyclic graphs (Bayesian networks). By the end you will be able to represent dependency structures in data with graphical models and select appropriate model classes.9 ECTSno date this semesterMA5439Grundlagen von Computer VisionNo ratings for this module yet.B1.1, B2.1, B3You will learn how images are formed and how to process image information for measurement and control tasks. The module covers surface properties, camera models and lenses as well as basic image processing and 3D reconstruction methods (stereo, shape from shading, structure-from-motion). In the end you will be able to calibrate camera systems and assess and apply methods for 3D reconstruction and image processing for robotic applications.4 ECTSno date this semesterIN2133Introduction to Mobile RoboticsNo ratings for this module yet.B1.1, B2.1, B3You will learn how mobile, especially wheel-driven robots perceive their environment, map it, and move autonomously within it. In the end you can develop probabilistic sensor and motion models, apply filter methods for localization and SLAM, and implement fundamental methods for path planning and obstacle avoidance.6 ECTSno date this semesterCIT3330000Machine Learning for Graphs and Sequential DataNo ratings for this module yet.B1.1, B2.1, B3You will learn methods of machine learning for graph data and sequential data. You understand models for texts and temporal sequences as well as techniques for networks and graphs and can apply and evaluate them to real tasks. In the end you can select suitable procedures and assess their strengths/weaknesses for non-independent data.5 ECTSno date this semesterIN2323Maschinelles Lernen für ComputersehenNo ratings for this module yet.B1.1, B2.1, B3You will learn fundamental machine learning methods that are frequently used in computer vision (e.g., object classification, segmentation, denoising, camera calibration). In the end you will be able to explain the mathematical formulation of central procedures, create simple implementations, and apply them to concrete datasets from the field of computer vision.5 ECTSno date this semesterIN2357Mathematical Data AnalysisNo ratings for this module yet.B1.1, B2.1, B3You will learn methods for analyzing complex and unstructured data, including regression and classification tasks. The module provides fundamentals such as loss functions, positive definite kernels and reproducing kernel Hilbert spaces as well as regularization strategies (e.g. Tikhonov) and SVM regression. In addition, summation procedures and manifold learning are covered.6 ECTSno date this semesterMA5098Mathematical Foundations of Machine LearningNo ratings for this module yet.B1.1, B2.1, B3You will learn the mathematical foundations of modern machine learning methods: structure, learning algorithms and approximation properties of neural networks, theory and application of kernel methods in reproducing kernel Hilbert spaces as well as qualitative aspects such as loss functions, risk and complexity questions. By the end you will be able to construct networks, discuss their approximation properties, apply kernel methods and assess the statistical efficiency of procedures.6 ECTSno date this semesterMA4801Mathematical Foundations of Machine LearningNo ratings for this module yet.B1.1, B2.1, B3You will learn the mathematical foundations of modern methods of machine learning. By the end you can design neural networks and discuss their approximation properties, apply kernel methods in Reproducing Kernel Hilbert Spaces, and assess the statistical efficiency of learning procedures.9 ECTSno date this semesterCIT413048Modellbildung und Simulation (Fokus Analysis)No ratings for this module yet.B1.1, B2.1, B3You will learn the fundamentals of multi-dimensional analysis as well as techniques of mathematical modeling and simulation. In the end you will be able to apply multidimensional derivatives, partial differential equations and integration methods, derive simple formal models (mathematical or computational) from verbal tasks, and select and implement suitable simulation strategies.9 ECTSno date this semesterIN2366Modelling and SimulationNo ratings for this module yet.B1.1, B2.1, B3You learn how to transfer real problems into formal models (mathematical or computational) and to process these models with suitable simulation strategies computationally. By the end of the module you will be able to distinguish model classes, develop simple solution procedures and select and apply simulation methods.8 ECTSno date this semesterIN2010Modern Methods in Nonlinear OptimizationNo ratings for this module yet.B1.1, B2.1, B3You will learn selected modern methods of nonlinear optimization, e.g. convex and non-smooth optimization, interior-point methods, semidefinite programming, robustness concepts and duality. In the end you will be able to understand current research articles on the treated topics and you will be prepared to pursue your own research questions in nonlinear optimization.5 ECTSno date this semesterMA4503Parallel ProgrammingNo ratings for this module yet.B1.1, B2.1, B3You learn concepts and techniques for parallel programming for distributed and shared memory architectures. The focus is on MPI for distributed systems and OpenMP for shared memory; in addition, dependency analysis, program transformations and newer programming models (e.g. PGAS, CUDA, OpenCL, OpenACC) are covered. In the end you will be able to create programs with MPI and OpenMP, assess their performance and analyze and optimize parallelized applications.5 ECTSno date this semesterIN2147Polyhedral CombinatoricsNo ratings for this module yet.B1.1, B2.1, B3You learn how to approach combinatorial optimization problems through the geometry of polyhedra: representation of polytopes, the connection between geometry and optimization of linear functions, as well as modern algorithms such as branch-and-cut and separation/optimization. In the end you will be able to apply the methods to typical problems (e.g., matching, TSP polytopes) and assess their limits in the context of NP-hardness.6 ECTSno date this semesterMA5225Probabilistische Graphische Modelle in der Computer VisionNo ratings for this module yet.B1.1, B2.1, B3You learn how probabilistic graphical models (directed and undirected) are used to model and solve typical computer vision problems. At the end you can understand MRF/CRF models, select appropriate inference and learning methods, and apply them to tasks such as segmentation, pose estimation, stereo or object recognition.5 ECTSno date this semesterIN2329Robust Machine LearningNo ratings for this module yet.B1.1, B2.1, B3You learn the basics and current methods of robust machine learning (ML). The module covers types of attacks and threat models, practical attack methods, as well as empirical and certifiable defenses. In the end you will be able to assess methods for securing ML systems and select appropriate algorithms for concrete problems.3 ECTSno date this semesterCIT423004Signal Processing and Machine LearningNo ratings for this module yet.B1.1, B2.1, B3You will learn advanced mathematical methods, concepts and algorithms from signal processing and machine learning and apply them. The focus is on the integration of both paradigms with applications in communication and data processing. In the end, you can reformulate typical problem statements, apply suitable algorithms (e.g., for sparse signal processing or neural networks) and evaluate their results.5 ECTSno date this semesterEI70380TopologyNo ratings for this module yet.B1.1, B2.1, B3You learn the fundamentals of set-theoretic topology and algebraic topology. In the first part you analyze topological and metric spaces as well as concepts such as continuity, compactness and connectedness; in the second part you deal with homotopies, fundamental groups and singular homology. At the end of the module you can assess topological properties as well as determine fundamental groups and homology of simple spaces.9 ECTSno date this semesterMA3241
3 more modules match, but they are taught in German. Show themBildverstehen I: Methoden der industriellen BildverarbeitungNo ratings for this module yet.B1.1, B2.1, B3Du lernst praxisrelevante Methoden und Algorithmen der industriellen Bildverarbeitung kennen. Im Modul werden Verfahren zur Lageerkennung, Form- und Maßprüfung sowie Objekterkennung behandelt. Am Ende kannst du Bildverarbeitungsaufgaben analysieren, bewerten und mit geeigneter Hardware und Algorithmen umsetzen.3 ECTSno date this semesterIN2023Einsatz und Realisierung von DatenbanksystemenNo ratings for this module yet.B1.1, B2.1, B3Du lernst, wie moderne Datenbanksysteme aufgebaut und eingesetzt werden. Am Ende kannst Du wesentliche Komponenten (Transaktionsverwaltung, Recovery, Mehrbenutzersynchronisation, physische Organisation, Anfragebearbeitung) erklären, Algorithmen und Datenstrukturen implementieren sowie Einsatzszenarien kritisch bewerten und skizzieren.6 ECTSno date this semesterIN2031Industrielle BildverarbeitungNo ratings for this module yet.B1.1, B2.1, B3In dem Modul lernst Du praxisrelevante Methoden und Algorithmen der industriellen Bildverarbeitung kennen. Du verstehst die typischen Einsatzgebiete (z. B. Lageerkennung, Form- und Maßprüfung, Beschriftungs- und Objekterkennung) und kannst Bildverarbeitungsaufgaben analysieren, bewerten und Lösungen entwickeln.6 ECTSruns this semesterIN2369