Modules

41 results

Further Elective Courses41

Advanced Robot Learning and Decision-MakingNo ratings for this module yet.Data Driven Simulation and ComputingYou will learn advanced methods for robotics that connect modeling, optimal control and learning-based methods (including reinforcement learning). In the end you will be able to model robotic systems, derive modern control algorithms and apply learned models in control architectures to handle uncertainties and model errors.5 ECTSruns this semesterCIT433037Computer Vision III: Detektion, Segmentierung und TrackingNo ratings for this module yet.Vision and VisualizationYou will learn modern methods for object recognition, segmentation and tracking in images and videos. In the end you will understand the underlying deep-learning concepts and be able to work on real computer-vision problems with state-of-the-art models in PyTorch.6 ECTSruns this semesterIN2375Einführung in die Funktionalanalysis (BV/COME)No ratings for this module yet.Numerical MethodsYou will learn the fundamental concepts and structures of functional analysis for real and complex function spaces (metric, norm, inner product space, classical function spaces, linear mappings). Building on that, you will cover topological concepts such as convergence, continuity and completeness and apply the theory to boundary value problems (Sobolev spaces, variational formulation, Galerkin method). In the end you will understand the mathematical foundations of the finite element method and be able to use function spaces as abstractions of familiar linear and analytical objects.5 ECTSruns this semesterMA9304Einführung in Quantum ComputingNo ratings for this module yet.Quantum ComputingYou will learn the mathematical foundations of Quantum Computing as well as the basic concepts of quantum mechanics and quantum circuits. In the end you will be able to analyze simple quantum algorithms, design and assess quantum circuits for fundamental tasks, and understand how quantum computers may be used in the future.5 ECTSruns this semesterIN2381Financial Mathematics 1No ratings for this module yet.Probabilistic Methods in CSEYou will learn the fundamentals of mathematical finance in discrete time: pricing and valuation of derivatives in one- and multi-period models, concepts of arbitrage and completeness, as well as basics of portfolio optimization. By the end you will be able to understand price models and implement them numerically, and analyze and optimize portfolios according to risk-return criteria.9 ECTSruns this semesterMA3407
36 more in Further Elective CoursesImage SynthesisNo ratings for this module yet.Vision and VisualizationYou learn modern techniques of image synthesis from the programmable graphics pipeline through shader and multi-pass methods to local and global illumination models. In the end you will be able to classify different rendering methods, evaluate existing image synthesis tools, and create renderings for given models and scenes yourself.5 ECTSruns this semesterIN2015Introduction to Deep LearningNo ratings for this module yet.Data Driven Simulation and ComputingIn this module you will learn the fundamentals and current methods of Deep Learning, with a special focus on neural networks and Convolutional Neural Networks. You understand theory (e.g. backpropagation, SGD, regularization) and acquire practical experience in training and optimizing network architectures, so you can solve simple applications such as digit recognition or image classification.6 ECTSruns this semesterIN2346Machine LearningNo ratings for this module yet.Data Driven Simulation and ComputingYou 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 semesterIN2064Maschinelles Lernen in der ErdsystemmodellierungNo ratings for this module yet.Data Driven Simulation and ComputingYou will learn data-driven and process-based approaches to modeling the Earth system and its components. In the end you will be able to name classical process-based models and various machine-learning methods for describing Earth system dynamics, apply interpretable AI methods, and critically evaluate approaches that combine process- and data-driven models.3 ECTSruns this semesterED110065Parallel Programming SystemsNo ratings for this module yet.Parallel and Distributed Computing, High Performance ComputingYou learn how parallel programming models are efficiently implemented and how compilers and runtimes map these models to real multi-core architectures. In the end you will know how fundamental algorithms and mechanisms (e.g. locks, barriers, scheduling) work and how they interact with processor architectures, illustrated by the Intel architecture.3 ECTSruns this semesterIN2365Probabilistische Techniken und Algorithmen in der DatenanalyseNo ratings for this module yet.Data Driven Simulation and ComputingYou will learn probabilistic techniques and algorithms used in data analysis for dimensionality reduction and data recovery from incomplete information. In the end, you will understand the basics of random matrices, assess randomized algorithms, and apply and analyze methods such as JL embeddings, compressed sensing, and randomness-based matrix/tensor reconstruction.6 ECTSruns this semesterMA4803Probability Theory and Uncertainty QuantificationNo ratings for this module yet.Probabilistic Methods in CSEYou will learn methods for modeling stochastic behavior in physical and technical systems (probabilistic modeling) and techniques for quantifying the resulting uncertainties. The focus is on uncertainty propagation through numerical models, backpropagation of uncertainties via data assimilation and Bayesian calibration, as well as optimization and design under uncertainty.5 ECTSruns this semesterMW23601.000+ ProjektwocheNo ratings for this module yet.Algorithms in Scientific ComputingYou work in an interdisciplinary student team for one week directly with a company partner (SME or industry) on a real operational question. In the end you can assess operational processes and corporate culture, apply innovation processes, develop practically oriented solution strategies, and work across disciplines.3 ECTSno date this semesterCIT643002Advanced Deep Learning for PhysicsNo ratings for this module yet.Data Driven Simulation and ComputingIn this module you will learn deep-learning methods and numerical simulation algorithms for physical materials such as fluids and deformable bodies. By the end you will be able to apply concepts such as generative models, time-series prediction, and numerical methods for partial differential equations, and select suitable network architectures and solvers for concrete tasks.6 ECTSno date this semesterIN2298Advanced Parallel Computing and Solvers for Large Problems in EngineeringNo ratings for this module yet.Parallel and Distributed Computing, High Performance ComputingYou learn how parallel computers are used for large-scale simulations of structure and fluid mechanics. You will gain an overview of methods, algorithms and software for multi-processor architectures and, in the end, you will be able to assess and design basic parallel solution approaches for very large systems of equations.5 ECTSno date this semesterMW1746Algorithmen für Uncertainty QuantificationNo ratings for this module yet.Probabilistic Methods in CSEYou will learn methods for quantifying uncertainties in computer-based simulations. In the end you will be able to place central algorithms for Forward Uncertainty Quantification, assess their complexity, and evaluate when which procedures (e.g., Monte Carlo, Quasi-Monte Carlo, stochastic collocation, stochastic Galerkin) are suitable.5 ECTSno date this semesterIN2345Algorithmic Game TheoryNo ratings for this module yet.Algorithms in Scientific ComputingYou will learn the fundamentals of algorithmic game theory at the intersection of computer science, mathematics, and economics. In this module you will deal with algorithmic aspects of game-theoretic solution concepts such as Nash equilibria and with the design of economic mechanisms; in the end you will be able to analyze these concepts algorithmically and in terms of complexity theory.5 ECTSno date this semesterIN2239Algorithms for Scientific ComputingNo ratings for this module yet.Algorithms in Scientific ComputingYou will learn efficient, hierarchical algorithms and data structures for scientific computing and how to implement them. The focus is on fast discrete Fourier and related transforms (FFT, DCT/DST), space-filling curves (Peano, Hilbert) for organizing multi-dimensional data, as well as hierarchical methods such as Sparse Grids and adaptive representations. In the end you will be able to explain such procedures, analyze them, and implement them if needed.8 ECTSno date this semesterIN2001Anwendungen von QuantencomputingNo ratings for this module yet.Quantum Computing5 ECTSno date this semesterNAT7022Augmented RealityNo ratings for this module yet.Vision and VisualizationYou learn the fundamentals of augmented reality (AR): geometric transformations, projection geometry, representation of 3D information, display and tracking devices, as well as mathematical foundations of optical tracking, sensor fusion, and calibration. In the end you can describe, analyze and extend the mathematical and programming aspects of AR systems for your own solution approaches, and evaluate input/output devices for suitability to application scenarios.6 ECTSno date this semesterIN2018Basic Mathematical Methods for Imaging and VisualizationNo ratings for this module yet.Vision and VisualizationYou 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 semesterIN2124Cloud-Based Data ProcessingNo ratings for this module yet.Parallel and Distributed Computing, High Performance ComputingYou 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.Probabilistic Methods in CSEYou 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 semesterMA4402Computer Vision I: Variational MethodsNo ratings for this module yet.Vision and VisualizationYou learn how many tasks of image processing (e.g. denoising, desmearing, segmentation, optical flow, stereo depth estimation, 3D reconstruction) are formulated and solved as variational problems. In the end you know the Euler–Lagrange approach and PDEs, efficient solution methods as well as convex formulations and relaxations and you can implement central concepts in Matlab.8 ECTSno date this semesterIN2246Distributed SystemsNo ratings for this module yet.Parallel and Distributed Computing, High Performance ComputingYou 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 semesterIN2259Fortgeschrittene Konzepte des Quantum ComputingNo ratings for this module yet.Quantum ComputingYou will learn advanced concepts and algorithms of Quantum Computing, with a focus on the quantum Fourier transform, the Shor algorithm and quantum error correction. In the end you will be able to distinguish these techniques, apply the quantum Fourier transform in new scenarios and understand the mathematical formalism of error correction, including the stabilizer formalism.5 ECTSno date this semesterIN2400Generalized Linear ModelsNo ratings for this module yet.Probabilistic Methods in CSEYou 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 semesterMA3403Geometry ProcessingNo ratings for this module yet.Vision and VisualizationYou will learn mathematical foundations and practical representations of curves and surfaces, as well as methods for modeling, analysis, and presentation of geometric shapes. In the end you can assess various representations (parametric, implicit, splines, subdivision, CSG, voxel, neural), reconstruction from point sets, and level-of-detail strategies, and independently develop and implement suitable approaches.6 ECTSno date this semesterIN2297Innovative Computing for AINo ratings for this module yet.Data Driven Simulation and ComputingThe module conveys the limits of technological scaling and shows which novel concepts in hardware, memory and architecture are needed to overcome these limits. By the end you will know current Beyond-CMOS technologies, novel memory, in-memory/near-memory and neuromorphic approaches as well as brain-inspired algorithms (e.g., Hyperdimensional Computing) and their implications for performance, energy and reliability.6 ECTSno date this semesterCIT4330016Konvexe Optimierung für Computer VisionNo ratings for this module yet.Vision and VisualizationYou will learn the fundamentals of convex analysis and their application to optimization problems in image processing and computer vision. After the module you will be able to understand, apply and implement common first-order and proximal methods for typical CV tasks (e.g., image reconstruction, segmentation, matrix factorization).6 ECTSno date this semesterIN2330Machine Learning for Graphs and Sequential DataNo ratings for this module yet.Data Driven Simulation and ComputingYou 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 semesterIN2323Modellierung und Maschinelles Lernen von dynamischen SystemenNo ratings for this module yet.Data Driven Simulation and ComputingYou will learn to use the Julia programming language for the analysis and simulation of nonlinear and chaotic dynamical systems. In the end you can implement simple nonlinear models in Julia, analyze time series of dynamical systems, and apply data-driven modeling approaches — with examples from Earth system and climate modeling.5 ECTSno date this semesterED110068Numerical Methods for Partial Differential EquationsNo ratings for this module yet.Numerical MethodsYou will learn numerical solution methods for partial differential equations, in particular finite element methods for multi-dimensional elliptic boundary value problems. You will also learn error estimates, adaptive mesh refinement, fast solvers and an introduction to numerical methods for time-dependent problems. In the end you will be able to understand the methods, apply them and use the associated software.9 ECTSno date this semesterMA3303Parallel Program EngineeringNo ratings for this module yet.Parallel and Distributed Computing, High Performance ComputingYou learn methodically and practically how to develop, analyze, and optimize parallel applications. The module covers programming models, development tools and workflows for performance engineering, so that you can design and implement structured solutions for the development and enhancement of parallel software at the end.5 ECTSno date this semesterIN2310Probabilistische Graphische Modelle in der Computer VisionNo ratings for this module yet.Vision and VisualizationYou 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 semesterIN2329QuanteninformationNo ratings for this module yet.Quantum ComputingYou will learn the fundamentals of quantum information theory: states and operations of qubits, entanglement, simple quantum algorithms, quantum measurement, quantum coding and elements of quantum error correction. In the end you will be able to apply fundamental concepts such as Schmidt decomposition, PPT criterion, Choi–Jamiołkowski isomorphism and simple quantum protocols and design simple circuits.10 ECTSno date this semesterNAT3035Quantum Computers and Quantum Secure CommunicationsNo ratings for this module yet.Quantum ComputingYou will learn the fundamentals of quantum computing and post-quantum cryptography, including quantum and post-quantum algorithms. In the end you will be able to evaluate the security and performance of post-quantum cryptographic implementations and apply appropriate security measures.5 ECTSno date this semesterEI71073Scientific Computing and Machine LearningNo ratings for this module yet.Data Driven Simulation and ComputingYou will learn how methods of Machine Learning are applied in Scientific Computing to solve problems from the natural sciences and engineering. The focus is on numerical approximations of differential equations, inverse problems, model reduction and practical implementation on CPU/GPU. In the end you will be able to understand mathematical formulations of dynamic systems and select appropriate learning algorithms for specific problems.5 ECTSno date this semesterCIT423000TensornetzwerkeNo ratings for this module yet.Quantum ComputingYou will learn the mathematical foundations and the graphical notation of tensor networks and their application to the approximation of high-dimensional data. In the end you will be able to assess tensor network methods and apply them to problems such as simulation of strongly correlated quantum systems or probabilistic sampling.5 ECTSno date this semesterIN2388TUM Data Innovation LabNo ratings for this module yet.Data Driven Simulation and ComputingIn the TUM Data Innovation Lab you work in your Master's program in small, interdisciplinary teams on real data-driven projects from science or industry. In the end you will be able to process, analyze and visualize data, implement numerical solutions, and present your results both technically and in an understandable way.10 ECTSno date this semesterMA8113Weiterführende Finite-Elemente MethodenNo ratings for this module yet.Numerical MethodsYou will learn about advanced finite element techniques (e.g. Mixed/Hybrid Elements, Discontinuous Galerkin, Non-conforming methods, adaptive procedures, Isogeometric Analysis) as well as modern iterative solvers and preconditioners. In the end you will be able to analyze these methods, apply them to examples from solid mechanics and incompressible flow, and independently access further specialist literature.5 ECTSno date this semesterMA4303
1 more module matches, but it is taught in German. Show itQuanten-Entrepreneurship-LaborNo ratings for this module yet.Quantum ComputingDu arbeitest praxisorientiert an Fragestellungen aus dem Bereich Quanten-Entrepreneurship. Am Ende kannst du deine erarbeiteten Ergebnisse in einer fachlichen Präsentation vorstellen und die zugrundeliegenden Konzepte und Ergebnisse in der Diskussion vertreten.6 ECTSno date this semesterPH8128