Suchergebnis: Katalogdaten im Frühjahrssemester 2018
Mikro- und Nanosysteme Master | ||||||
Kernfächer | ||||||
Empfohlene Kernfächer | ||||||
Modelling and Simulation | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|---|
401-3632-00L | Computational Statistics | W | 10 KP | 3V + 2U | M. H. Maathuis | |
Kurzbeschreibung | Computational Statistics deals with modern statistical methods of data analysis (aka "data science") for prediction and inference. The course provides an overview of existing methods. The course is hands-on, and methods are applied using the statistical programming language R. | |||||
Lernziel | In this class, the student obtains an overview of modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties. The methods are applied using the statistical programming language R. | |||||
Voraussetzungen / Besonderes | At least one semester of (basic) probability and statistics. Programming experience is helpful but not required. | |||||
151-0116-10L | High Performance Computing for Science and Engineering (HPCSE) for Engineers II | W | 4 KP | 4G | P. Koumoutsakos, P. Chatzidoukas | |
Kurzbeschreibung | This course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Uncertainty Quantification and Propagation including the implementation of relevant algorithms on HPC architectures. | |||||
Lernziel | The course will teach - programming models and tools for multi and many-core architectures - fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences | |||||
Inhalt | High Performance Computing: - Advanced topics in shared-memory programming - Advanced topics in MPI - GPU architectures and CUDA programming Uncertainty Quantification: - Uncertainty quantification under parametric and non-parametric modeling uncertainty - Bayesian inference with model class assessment - Markov Chain Monte Carlo simulation | |||||
Skript | Link Class notes, handouts | |||||
Literatur | - Class notes - Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein - CUDA by example, J. Sanders and E. Kandrot - Data Analysis: A Bayesian Tutorial, Devinderjit Sivia |
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