Sergio Miguel Martin: Katalogdaten im Frühjahrssemester 2022 |
| Name | Herr Dr. Sergio Miguel Martin |
| Adresse | Dep. Maschinenbau und Verf.technik ETH Zürich, CLT E 13 Clausiusstrasse 33 8092 Zürich SWITZERLAND |
| martiser@ethz.ch | |
| Departement | Maschinenbau und Verfahrenstechnik |
| Beziehung | Dozent |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 151-0116-00L | High Performance Computing for Science and Engineering (HPCSE) for CSE  | 7 KP | 4G + 2P | P. Koumoutsakos, S. M. Martin | |
| 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 Bayesian Uncertainty Quantification and Machine Learning including the implementation of these 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. - fundamentals of Deep Learning | ||||
| 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 Machine Learning - Deep Neural Networks and Stochastic Gradient Descent - Deep Neural Networks for Data Compression (Autoencoders) - Recurrent Neural Networks | ||||
| Skript | https://www.cse-lab.ethz.ch/teaching/hpcse-ii_fs22/ 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, D. Sivia and J. Skilling - An introduction to Bayesian Analysis - Theory and Methods, J. Gosh, N. Delampady and S. Tapas - Bayesian Data Analysis, A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari and D. Rubin - Machine Learning: A Bayesian and Optimization Perspective, S. Theodorides | ||||
| Voraussetzungen / Besonderes | Attendance of HPCSE I | ||||
| 151-0116-10L | High Performance Computing for Science and Engineering (HPCSE) for Engineers II | 4 KP | 4G | P. Koumoutsakos, S. M. Martin | |
| 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 | https://www.cse-lab.ethz.ch/teaching/hpcse-ii_fs22/ 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, D. Sivia and J. Skilling - An introduction to Bayesian Analysis - Theory and Methods, J. Gosh, N. Delampady and S. Tapas - Bayesian Data Analysis, A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari and D. Rubin - Machine Learning: A Bayesian and Optimization Perspective, S. Theodorides | ||||
| Voraussetzungen / Besonderes | Students must be familiar with the content of High Performance Computing for Science and Engineering I (151-0107-20L) | ||||