Modules

40 results

Elective Modules40

Advanced Deep Learning for Computer Vision: Visual ComputingNo ratings for this module yet.Special Topics in Data AnalyticsThe module conveys current deep-learning methods for computer vision. You will learn both theoretical foundations of modern neural architectures and practical methods such as generative models (GANs, diffusion models, Large Reconstruction Models) and modern 3D representations (e.g., Neural Radiance Fields, 3D Gaussian Splatting). In the end you will be able to apply, evaluate, and implement state-of-the-art methods in project work.8 ECTSruns this semesterIN2390Computer Vision III: Detektion, Segmentierung und TrackingNo ratings for this module yet.Special Topics in Data AnalyticsYou 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 semesterIN2375Efficient Algorithms and Data StructuresNo ratings for this module yet.Special Topics in Data AnalyticsYou will learn the fundamentals of analyzing algorithms as well as central data structures and fundamental algorithmic problems. The focus is on runtime and space analysis, various search trees, hashing methods, priority queues, union-find structures, as well as maxflow/mincut and matching algorithms. In the end you will be able to analyze algorithms, assess the efficiency of data structures, and design new solutions for problems.8 ECTSruns this semesterIN2003Introduction to Deep LearningNo ratings for this module yet.Special Topics in Data AnalyticsIn 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 semesterIN2346Introduction to Deep Reinforcement LearningNo ratings for this module yet.Special Topics in Data AnalyticsYou learn the theory and fundamentals of Deep Reinforcement Learning (DRL) as well as relevant deep-learning and reinforcement-learning concepts. By the end you will be able to model problems as Markov Decision Processes, understand and apply DRL algorithms, and assess their advantages and disadvantages.6 ECTSruns this semesterMGT001299
35 more in Elective ModulesMachine Learning for 3D GeometryNo ratings for this module yet.Special Topics in Data AnalyticsYou will learn theoretical foundations and modern machine‑learning methods for 3D geometric data. The module covers representations of shapes and scenes as well as deep‑learning architectures for discriminative and generative tasks such as classification, segmentation, reconstruction and synthesis. In the end you will be able to understand and practically apply common approaches to point clouds, volumetric data, multi‑view inputs and graphs.6 ECTSruns this semesterIN2392Machine Learning for Electronic Design Automation and ManufacturingNo ratings for this module yet.Special Topics in Data AnalyticsYou will learn how machine-learning methods can support and improve the development, design, testing, and manufacturing of chips. The module combines theoretical fundamentals with current procedures and hands-on exercises, so you can apply machine-learning approaches to concrete design automation and manufacturing tasks.5 ECTSruns this semesterCIT433031Natural Language ProcessingNo ratings for this module yet.Special Topics in Data AnalyticsYou will receive a solid introduction to modern Natural Language Processing (NLP) methods. In the end you will know central concepts, algorithms and models from tokenizing, through parsing, embeddings and NER, to modern neural approaches and you will be able to read, analyze and contextualize scientific publications in the discipline for your own projects or theses.6 ECTSruns this semesterIN2361Trustworthy Distributed LearningNo ratings for this module yet.Special Topics in Data AnalyticsYou will learn the fundamentals and current research on distributed learning (centralized, federated, decentralized) with a focus on data privacy, robustness against malicious clients, and reduction of communication costs. In the end you will be able to analyze, implement yourself, and critically evaluate algorithms for privacy, robustness, and communication efficiency.6 ECTSruns this semesterCIT433044Trustworthy Machine Learning SystemsNo ratings for this module yet.Special Topics in Data AnalyticsYou learn how to design and evaluate systems that provide trustworthy functions with machine-learning components. In the end you will be able to assess the necessity, benefits and challenges of such systems in various application fields, as well as analyze risks and derive appropriate measures to create trustworthy ML components.3 ECTSruns this semesterCIT4330017Advanced Deep Learning for Computer Vision: Dynamic VisionNo ratings for this module yet.Special Topics in Data AnalyticsYou will learn advanced deep-learning methods with a clear focus on video analysis for computer-vision tasks. Beyond the theoretical foundations, you will train and implement neural networks yourself and work on a semester-long project on current research topics of the group.8 ECTSno date this semesterIN2389Advanced Deep Learning for PhysicsNo ratings for this module yet.Special Topics in Data AnalyticsIn 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 Machine Learning: Deep Generative ModelsNo ratings for this module yet.Special Topics in Data AnalyticsYou will engage with advanced methods of machine learning, with a focus on deep generative models. In the end you will know the theoretical foundations and be able to apply and implement the key building blocks (Normalizing Flows, VAEs, GANs, diffusion models) in a modern programming language and qualitatively compare them.3 ECTSno date this semesterCIT4230003Advanced Natural Language ProcessingNo ratings for this module yet.Special Topics in Data AnalyticsYou engage with current, advanced topics in Natural Language Processing: modern transformer models and explainability, machine translation for resource-poor languages, argument mining and ethical aspects, dialogue-oriented AI, quantum NLP, automatic text summarization as well as multimodal models (text + images). In the end you will be able to understand research work on these methods, combine technical building blocks and design and evaluate own architectural variants for research or practical application.5 ECTSno date this semesterCIT4230002Algorithmen für Uncertainty QuantificationNo ratings for this module yet.Special Topics in Data AnalyticsYou 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.Special Topics in Data AnalyticsYou 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.Special Topics in Data AnalyticsYou 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 semesterIN2001Algorithms for Scientific Computing IINo ratings for this module yet.Special Topics in Data AnalyticsYou engage with advanced numerical methods and their application in scientific computing. Depending on the lecture focus, you learn e.g. efficient procedures for sparsely populated matrices, numerical techniques for quantum systems, or methods such as molecular dynamics, sparse grids and algebraic multigrid. In the end you can apply suitable methods to demanding application problems and explain and utilize their hierarchical aspects.4 ECTSno date this semesterIN2002Applied Reinforcement LearningNo ratings for this module yet.Special Topics in Data AnalyticsYou will learn practical methods of reinforcement learning (RL) for sequential decision problems and how to apply them. In the end you will be able to model typical RL scenarios, implement common algorithms, and solve and assess simple robotics tasks (e.g. on the e-Puck) with RL.6 ECTSno date this semesterEI7641Artificial Intelligence in Medicine IINo ratings for this module yet.Special Topics in Data AnalyticsYou will gain an overview of advanced prediction and classification tasks in medicine. You will learn methods for prognosis and diagnostics (e.g., risk scores, survival models, differential diagnosis, population stratification), specialized ML techniques (geometric deep learning methods for point clouds/networks, transformers, reinforcement learning) as well as topics on trustworthiness and clinical implementation of AI (bias, fairness, generalizability, data harmonization, evaluation). By the end you can apply the concepts in your own AI projects and assess their social and ethical implications.5 ECTSno date this semesterIN2408Causal Inference in Time SeriesNo ratings for this module yet.Special Topics in Data AnalyticsYou 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 semesterCIT4230006Complexity TheoryNo ratings for this module yet.Special Topics in Data AnalyticsYou learn formal computational models (in particular Turing machines and circuits) as well as the most important complexity classes (e.g. L, NL, P, NP, PSPACE, EXP, NEXP, PH). By the end you will be able to analyze problems with respect to time and space complexity, apply reductions and completeness proofs, and classify advanced concepts such as alternation, randomized methods, and interactive proof systems.8 ECTSno date this semesterIN2007Compressive SamplingNo ratings for this module yet.Special Topics in Data AnalyticsYou learn the fundamentals of Compressive Sampling (Compressed Sensing): how to efficiently measure and reconstruct signals with sparse parametrization. By the end you will be able to apply the theoretical concepts and the treated algorithms for parameter estimation in the design and analysis of signal processing and estimation methods.5 ECTSno date this semesterEI7638Computational Social ChoiceNo ratings for this module yet.Special Topics in Data AnalyticsYou will become familiar with methods of collective decision making (Social Choice), with a focus on procedures that use majority relations and their algorithmic properties. In the end you will be able to analyze, compare, and assess the computational complexity of various election procedures.6 ECTSno date this semesterIN2229Computer Vision I: Variational MethodsNo ratings for this module yet.Special Topics in Data AnalyticsYou 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 semesterIN2246Computer Vision II: Multiple View Geometry (3D Computer Vision)No ratings for this module yet.Special Topics in Data AnalyticsYou will learn the mathematical foundations of multiple-view geometry in order to reconstruct camera motion and 3D geometry from several images. In the end you will understand image formation, epipolar geometry, camera calibration, rank conditions and bundle adjustment and you will be able to implement central algorithms in Matlab.8 ECTSno date this semesterIN2228Fundamentals of Foundation ModelsNo ratings for this module yet.Special Topics in Data AnalyticsYou learn what Foundation Models are (e.g., LLMs, CLIP, image generation) and how they are structured, trained and fine-tuned. In the end you understand architecture, scaling, data requirements and distributed training as well as strategies for fine-tuning and alignment/adaptation of models.5 ECTSno date this semesterCIT433021Machine Learning and IT-SecurityNo ratings for this module yet.Special Topics in Data AnalyticsYou will gain an overview of the intersection of Machine Learning and IT Security. You will learn how ML systems are used to detect attacks or spam/m malware, how ML systems themselves can be attacked and defended, and how audio deepfakes are produced and detected. In the end you will be able to design methods for anomaly detection, describe attacks on ML systems, and explain simple concepts of audio spoofing creation and detection.5 ECTSno date this semesterCIT4330001Machine Learning and OptimizationNo ratings for this module yet.Special Topics in Data AnalyticsYou will learn advanced concepts and methods of machine learning as well as common optimization procedures for their training. In the end you will be able to apply and further develop modern learning methods (e.g. deep neural networks), design optimization algorithms and analyze their behavior theoretically and empirically.5 ECTSno date this semesterEI70360Machine Learning for Graphs and Sequential DataNo ratings for this module yet.Special Topics in Data AnalyticsYou 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 semesterIN2323Machine Learning for Regulatory GenomicsNo ratings for this module yet.Special Topics in Data AnalyticsIn this module you will learn biological fundamentals of gene regulation and modern deep learning methods for modeling sequence-based regulatory processes. After completion you will be able to apply genome-wide experimental procedures and deep learning models for different stages of gene expression and biologically interpret the model predictions.6 ECTSno date this semesterIN2393Numerical Methods for Uncertainty QuantificationNo ratings for this module yet.Special Topics in Data AnalyticsYou learn how to formulate, analyze, and numerically approximate elliptic boundary value problems with random coefficients. This includes modeling and sampling of random fields as well as numerical methods such as Monte Carlo, stochastic collocation, and stochastic Galerkin methods.6 ECTSno date this semesterMA5348Numerische Algorithmen für Computer Vision und Maschinelles LernenNo ratings for this module yet.Special Topics in Data AnalyticsYou learn numerical methods that are frequently used in computer vision and machine learning, including modeling of practical problems. In the end you will be able to implement basic algorithms, assess their strengths and weaknesses, and choose appropriate methods for solving concrete CV-/ML tasks.5 ECTSno date this semesterIN2384Online- und ApproximationsalgorithmenNo ratings for this module yet.Special Topics in Data AnalyticsYou will learn fundamentals and advanced techniques of online and approximation algorithms. In the end you will know classical online problems (e.g., scheduling, paging, k-server), analysis tools such as amortized analysis and randomized algorithms, as well as design techniques for approximation algorithms including LP-relaxation and randomized rounding.8 ECTSno date this semesterIN2304Optimale Steuerung gewöhnlicher Differentialgleichungen 1No ratings for this module yet.Special Topics in Data AnalyticsYou learn fundamental concepts and methods of optimal control for ordinary differential equations. In the end you will be able to formulate necessary optimality conditions (e.g., Euler–Lagrange, Legendre–Clebsch), distinguish different types of constraints and control restrictions, and convert control problems into boundary-value forms suitable for numerical treatment.5 ECTSno date this semesterMA3312Parallele AlgorithmenNo ratings for this module yet.Special Topics in Data AnalyticsYou deal with models of parallel computation and develop fundamental parallel algorithms. In the end you know various machine models, master basic knowledge of parallel complexity theory, and can design and evaluate parallel algorithms.8 ECTSno date this semesterIN2011Quantitative VerificationNo ratings for this module yet.Special Topics in Data AnalyticsYou will learn how to formally model and analyze systems with quantitative aspects (e.g., time, probabilities). In the end you will be able to apply suitable model classes and specification languages, explain analysis algorithms and apply them to small examples practically, as well as use model checking tools.5 ECTSno date this semesterIN2340Randomisierte AlgorithmenNo ratings for this module yet.Special Topics in Data AnalyticsYou learn fundamentals and techniques of randomized algorithms and how to estimate their running time and correctness with probabilistic methods. In the end you will be able to understand and analyze classical randomized algorithms (e.g., randomized Quicksort, Min-Cut, Treaps) and apply tools such as Markov, Chebyshev and Chernoff inequalities.8 ECTSno date this semesterIN2160Robust Machine LearningNo ratings for this module yet.Special Topics in Data AnalyticsYou 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 semesterCIT423004Solving Inverse Problems with Deep LearningNo ratings for this module yet.Special Topics in Data AnalyticsYou will learn modern, evidence-based methods for solving inverse problems in imaging and signal processing. In the end you will be able to apply deep-learning approaches for the reconstruction of signals/images, assess their foundations and limits, and design your own variants of existing methods.6 ECTSno date this semesterEI71068
3 more modules match, but they are taught in German. Show themAnerkennung aus dem Bereich Special Topics in Data AnalyticsNo ratings for this module yet.Special Topics in Data Analyticsno date this semesterIN99560Anerkennung aus dem Bereich Special Topics in Data AnalyticsNo ratings for this module yet.Special Topics in Data Analyticsno date this semesterIN99561Anerkennung aus dem Bereich Special Topics in Data AnalyticsNo ratings for this module yet.Special Topics in Data Analyticsno date this semesterIN99562