Suchergebnis: Katalogdaten im Frühjahrssemester 2021

Rechnergestützte Wissenschaften Bachelor Information
Bachelor-Studium (Studienreglement 2016)
Kernfächer
NummerTitelTypECTSUmfangDozierende
151-0116-00LHigh Performance Computing for Science and Engineering (HPCSE) for CSE Information O7 KP4G + 2PP. Koumoutsakos, S. M. Martin
KurzbeschreibungThis 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.
LernzielThe 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
InhaltHigh 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
SkriptLink
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 / BesonderesAttendance of HPCSE I
252-0232-00LSoftware Engineering Information O6 KP2V + 1UF. Friedrich Wicker, M. Schwerhoff
KurzbeschreibungThis course introduces both theoretical and applied aspects of software engineering. It covers:

- Software Architecture
- Informal and formal Modeling
- Design Patterns
- Software Engineering Principles
- Code Refactoring
- Program Testing
LernzielThe course has two main objectives:

- Obtain an end-to-end (both, theoretical and practical) understanding of the core techniques used for building quality software.
- Be able to apply these techniques in practice.
InhaltWhile the lecture will provide the theoretical foundations for the various aspects of software engineering, the students will apply those techniques in project work that will span over the whole semester - involving all aspects of software engineering, from understanding requirements over design and implementation to deployment and change requests.
Skriptno lecture notes
LiteraturWill be announced in the lecture
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