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Trainingskurs "Parallel and Scalable Machine Learning"

(Kurs-Nr. 13620182 im Trainingsprogramm 2018 des Forschungszentrums)

06.03.2018 09:00 Uhr
08.03.2018 16:30 Uhr
Jülich Supercomputing Centre, Rotunde, Geb. 16.4, R. 301
Mitarbeiter, die Daten mit Machine Learning analysieren wollen

The course offers basics of analyzing data with machine learning and data mining algorithms in order to understand foundations of learning from large quantities of data. This course is especially oriented towards beginners that have no previous knowledge of machine learning techniques. The course consists of general methods for data analysis in order to understand clustering, classification, and regression. This includes a thorough discussion of test datasets, training datasets, and validation datasets required to learn from data with a high accuracy. Easy application examples will foster the theoretical course elements that also will illustrate problems like overfitting followed by mechanisms such as validation and regularization that prevent such problems.

The tutorial will start from a very simple application example in order to teach foundations like the role of features in data, linear separability, or decision boundaries for machine learning models. In particular this course will point to key challenges in analyzing large quantities of data sets (aka ‘big data’) in order to motivate the use of parallel and scalable machine learning algorithms that will be used in the course. The course targets specific challenges in analyzing large quantities of datasets that cannot be analyzed with traditional serial methods provided by tools such as R, SAS, or Matlab. This includes several challenges as part of the machine learning algorithms, the distribution of data, or the process of performing validation. The course will introduce selected solutions to overcome these challenges using parallel and scalable computing techniques based on the Message Passing Interface (MPI) and OpenMP that run on massively parallel High Performance Computing (HPC) platforms. The course ends with a more recent machine learning method known as deep learning that emerged as a promising disruptive approach, allowing knowledge discovery from large datasets in an unprecedented effectiveness and efficiency.

This course is a training course of the DEEP-EST project.

Job-Submissions zu großen HPC-Machines über Batch-Skripts, Kenntnisse der mathematischen Grundlagen in Linearer Algebra sind hilfreich.
Bitte bringen Sie Ihr eigenes Notebook mit (mit einem ssh-Client).
Der Kurs wird auf Englisch gehalten.
3 Tage
6. - 8. März 2018, 9.00-16.30 Uhr
Jülich Supercomputing Centre, Rotunde, Geb. 16.4, R. 301
maximal 40
Prof. Morris Riedel, JSC
Dr. Sabine Höfler-Thierfeldt
Bitte richten Sie Ihre Anmeldung an bis zum 20. Februar 2018. Dieser Kurs ist ausschließlich für Mitarbeiterinnen und Mitarbeiter des Forschungszentrums Jülich sowie des Projektes DEEP-EST.
Ankündigung als pdf-Datei:
 Parallel and Scalable Machine Learning (PDF, 31 kB)