Modules

22 results

Elective Modules Informatics22

Advanced Deep Learning for RoboticsNo ratings for this module yet.Machine Learning and Analytics (MLA)In 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 Analytics (MLA)You 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 Analytics (MLA)You 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 semesterLS20056Data Mining und Knowledge DiscoveryNo ratings for this module yet.Machine Learning and Analytics (MLA)You 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 semesterIN2030Foundations and Application of Generative AINo ratings for this module yet.Machine Learning and Analytics (MLA)You 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 semesterCITHN2014
17 more in Elective Modules InformaticsIntroduction to Deep LearningNo ratings for this module yet.Machine Learning and Analytics (MLA)In 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 semesterIN2346Künstliche Intelligenz in der MedizinNo ratings for this module yet.Machine Learning and Analytics (MLA)You 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 semesterIN2403Machine LearningNo ratings for this module yet.Machine Learning and Analytics (MLA)You 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 semesterIN2064Natural Language ProcessingNo ratings for this module yet.Machine Learning and Analytics (MLA)You 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 Analytics (MLA)You 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 Analytics (MLA)In 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 Analytics (MLA)You 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 Analytics (MLA)You 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 semesterCIT4230002Artificial Intelligence in Medicine IINo ratings for this module yet.Machine Learning and Analytics (MLA)You 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.Machine Learning and Analytics (MLA)You 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.Machine Learning and Analytics (MLA)You 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 semesterIN2410Fundamentals of Foundation ModelsNo ratings for this module yet.Machine Learning and Analytics (MLA)You 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 semesterCIT433021Legal Data Science and InformaticsNo ratings for this module yet.Machine Learning and Analytics (MLA)You 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 Analytics (MLA)You 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 Analytics (MLA)You 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 Analytics (MLA)You 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 Analytics (MLA)You 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
10 more modules match, but they are taught in German. Show themAnerkennung 1No ratings for this module yet.Machine Learning and Analytics (MLA)no date this semesterIN99555Anerkennung 1 MLA_THEONo ratings for this module yet.Machine Learning and Analytics (MLA)no date this semesterIN995076Anerkennung 2No ratings for this module yet.Machine Learning and Analytics (MLA)no date this semesterIN99556Anerkennung 2 MLA_THEONo ratings for this module yet.Machine Learning and Analytics (MLA)no date this semester995077Anerkennung 3 MLA_THEONo ratings for this module yet.Machine Learning and Analytics (MLA)no date this semesterIN995078Anerkennung 4No ratings for this module yet.Machine Learning and Analytics (MLA)no date this semesterIN99558Anerkennung 5No ratings for this module yet.Machine Learning and Analytics (MLA)no date this semesterIN99559Anerkennung im Bereich Maschinelles Lernen und DatenanalyseNo ratings for this module yet.Machine Learning and Analytics (MLA)no date this semesterIN99557Anerkennung im Bereich Maschinelles Lernen und Datenanalyse (Theorie)No ratings for this module yet.Machine Learning and Analytics (MLA)no date this semesterIN995077Ausgewählte Themen aus dem Bereich Maschinelles Lernen und DatenanalyseNo ratings for this module yet.Machine Learning and Analytics (MLA)5 ECTSno date this semesterIN3440