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Vorlesungsverzeichnis >> Medizinische Fakultät (Med) >>
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Parallel computing in machine learning- Dozentinnen/Dozenten
- Prof. Dr. Oliver Amft, Dr. rer. nat. Luis Ignacio Lopera Gonzales
- Angaben
- Seminar
4 SWS, benoteter Schein, Anwesenheitspflicht, ECTS-Studium, ECTS-Credits: 5, Sprache Englisch, Examination: Final project presentation, demonstrator and final report.
Zeit und Ort: n.V.; Bemerkung zu Zeit und Ort: First meeting (24.04.2019, 17:00 - 18:30) and seminar held at MVC, Henkestr. 91, 1st. Floor, R 373
Vorbesprechung: 24.4.2019, 17:00 - 18:30 Uhr
- ECTS-Informationen:
- Credits: 5
- Prerequisites
- Useful knowledge:
Python and data analytics
- Contents
- Seminar description:
In machine learning, tasks like parametric search or cross-validation are time intensive. In this seminar, we will explore how to use multithreading, multiprocessing, and compute clusters to reduce the execution time of machine learning frameworks. Additionally, we will cover some python basics and patterns to simplify parallel framework development.
The seminar has a heavy practical component to practice and become familiar with the challenges of parallel data processing and machine learning. Therefore, we invite participants to bring their own dataset for analysis, otherwise, we will provide a dataset for exploration.
Learning Objectives:
Gain an overview of general parallel processing tools and techniques.
Understand concepts of data processing, job distribution in machine learning frameworks.
Analyse machine learning frameworks in terms of data storage and processing.
Apply the parallel job approach to bigData problems.
Implement a distributed job handling solution for bigData and machine learning.
- Literature
- Up-to-date literature recommendations are provided during the lectures.
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 10, Maximale Teilnehmerzahl: 20
www: https://www.cdh.med.fau.de/2019/03/05/seminar-parallel-computing-in-machine-learning-2/
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt von Dienstag, 2.4.2019 bis Donnerstag, 18.4.2019 über: StudOn.
- Institution: Lehrstuhl für Digital Health
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