Modules

17 results

Cross-Cutting Elective Modules17

Advanced Deep Learning for RoboticsNo ratings for this module yet.Machine Learning and AnalyticsIn this module you deepen advanced deep learning methods with a focus on robotic applications and deep reinforcement learning. You learn both theoretical foundations of modern network architectures and probabilistic methods as well as their practical implementation in simulations and projects for robotics tasks.8 ECTSruns this semesterCIT433027Business Analytics and Machine LearningNo ratings for this module yet.Machine Learning and AnalyticsYou 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 semesterIN2028Computational NeuroscienceNo ratings for this module yet.Machine Learning and AnalyticsYou will learn concepts and implementations of central models and methods of Computational Neuroscience in Python. At the end you will be able to implement single-neuron and network models, analyze their properties, and transfer models to new questions, as well as apply fundamental methods of Machine Learning and information analysis.5 ECTSruns this semesterLS20056Foundations and Application of Generative AINo ratings for this module yet.Machine Learning and AnalyticsYou will learn the fundamentals and practical applications of generative AI, including modern models such as transformer architectures, Large Language Models, and Stable Diffusion. In the end you will be able to apply prompt engineering, load open-source models and fine-tune them, as well as assess opportunities, risks, and security aspects of generative AI.6 ECTSruns this semesterCITHN2014Introduction to Deep LearningNo ratings for this module yet.Machine Learning and 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 semesterIN2346
12 more in Cross-Cutting Elective ModulesKünstliche Intelligenz in der MedizinNo ratings for this module yet.Machine Learning and AnalyticsYou will learn the fundamentals and current methods of Artificial Intelligence in the medical context, from ML for medical imaging through NLP for clinical data to data protection, interpretability and ethical aspects. In the end you can classify the most important topics, apply them to your own deep-learning projects and develop strategies for evaluation and implementation in clinical practice.5 ECTSruns this semesterIN2403Natural Language ProcessingNo ratings for this module yet.Machine Learning and 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 semesterIN2361Statistical Foundations of LearningNo ratings for this module yet.Machine Learning and AnalyticsYou 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 semesterCIT4230004Advanced Deep Learning for PhysicsNo ratings for this module yet.Machine Learning and 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.Machine Learning and 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.Machine Learning and 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 semesterCIT4230002Causal Inference in Time SeriesNo ratings for this module yet.Machine Learning and 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 semesterCIT4230006Legal Data Science and InformaticsNo ratings for this module yet.Machine Learning and AnalyticsYou will learn how methods of Data Science and Artificial Intelligence are applied to legal questions, for example in analysis and search in legal documents, prediction of case outcomes and formal representations of legal knowledge. In the end you will be able to assess how ML and NLP techniques affect legal data, explain formal representation and argumentation models, and apply simple technical approaches to concrete, bounded problems.6 ECTSno date this semesterIN2395Machine Learning for Graphs and Sequential DataNo ratings for this module yet.Machine Learning and 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 semesterIN2323Maschinelles Lernen für ComputersehenNo ratings for this module yet.Machine Learning and AnalyticsYou 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 semesterIN2357NeuroAI and Machine Learning in NeuroscienceNo ratings for this module yet.Machine Learning and AnalyticsYou will learn modern methods of Computational Neuroscience and NeuroAI: from model-based approaches (dendrite models, mean-field, Fokker-Planck, linear response) through plasticity and stochastic processes to training methods for RNNs and SNNs as well as Reinforcement Learning and Predictive Coding. In the end you will be able to implement models at different levels, train RNNs/SNNs and analyze and reproduce NeuroAI papers in a professional manner.7 ECTSno date this semesterLS20057Robust Machine LearningNo ratings for this module yet.Machine Learning and 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 semesterCIT423004
4 more modules match, but they are taught in German. Show themAnerkanntes Wahlmodul im Bereich Maschinelles Lernen und DatenanalyseNo ratings for this module yet.Machine Learning and Analyticsno date this semesterIN99555Anerkanntes Wahlmodul im Bereich Maschinelles Lernen und DatenanalyseNo ratings for this module yet.Machine Learning and Analyticsno date this semesterIN99556Anerkanntes Wahlmodul im Bereich Maschinelles Lernen und DatenanalyseNo ratings for this module yet.Machine Learning and Analyticsno date this semesterIN99557Ausgewählte Themen aus dem Bereich Maschinelles Lernen und DatenanalyseNo ratings for this module yet.Machine Learning and Analytics5 ECTSno date this semesterIN3440