Morris Riedel Teaching 0

Teaching – Overview

2018 Upcoming: Cloud Computing and Big Data 18 university lectures with additional practical lectures for hands-on exercises in context University of Iceland, School of Engineering and Natural Sciences Faculty of Industrial Engineering, Mechanical Engineering and Computer Science Fall 2018 2017 High Performance Computing 18 university lectures with additional practical lectures for hands-on exercises in context University of Iceland, School of Engineering and Natural Sciences Faculty of Industrial Engineering, Mechanical Engineering and Computer Science Fall 2017 Cloud Computing and Big Data 18 university lectures with additional practical lectures for hands-on exercises in context University of Iceland, School of Engineering and Natural...

Morris Riedel Talks

Talks – Overview

2018 Tutorial: Parallel and Scalable Machine Learning Invited Tutorial PRACE Advanced Training Center, Juelich Supercomputing Centre, Germany 2018-01-15 – 2018-01-17 [ More ] 2016 TUTORIAL: Einführung in Maschinelles Lernen zur Datenanalyse Invited Tutorial (German language) Smart Data Innovation Conference, Karlsruhe Institute of Technology (KIT), Germany 2016-10-13 [ More ]

Morris Riedel Media 0

Media – Overview

2017 Invited Tutorial – Introduction to Machine Learning Algorithms Morris Riedel Ghent University, six lectures including exercises 2017-11-23 – 2017-11-24 [ Event ] Lecture 1 – Machine Learning Fundamentals Lecture 2 – Unsupervised Clustering and Applications Lecture 3 – Supervised Classification and Applications Lecture 4 – Classification Challenges and Solutions Lecture 5 – Regularization and Support Vector Machines Lecture 6 – Validation and Parallelization Benefits

Morris Riedel Publications

Publications – Overview

2017 Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay Christian Bodenstein, Markus Goetz, Annika Jansen, Henrike Scholz, Morris Riedel In conference proceedings of the 15th IEEE International Conference on Machine Learning and Applications, IEEE ICMLA’16, Anaheim, USA, 18 Dec 2016 – 20 Dec 2016, ISBN 978-1-5090-6167-9, pp. 746 – 751, 2017 [ DOI ] [ Juelich ] [ More ] 2016 Scientific Big Data Analytics by HPC Thomas Lippert, Daniel Mallmann, Morris Riedel Publication Series of the John von Neumann Institute for Computing (NIC) NIC Series 48, 417, ISBN 978-3-95806-109-5, pp. 1 – 10, 2016...

2018 Tutorial Parallel and Scalable Machine Learning 0

Tutorial: Parallel and Scalable Machine Learning

Tutorial: Parallel and Scalable Machine Learning Invited Tutorial PRACE Advanced Training Center, Juelich Supercomputing Centre, Germany 2018-01-15 – 2018-01-17 [ Event ] Abstract: 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...

Morris Riedel Biography 0

Biography

Prof. Dr. – Ing. Morris Riedel is an Adjunct Associate Professor at the School of Engineering and Natural Sciences of the University of Iceland. He received his PhD from the Karlsruhe Institute of Technology (KIT) and works in parallel and distributed systems since 15 years. He previously held various positions at the Juelich Supercomputing Centre in Germany. At this institute, he is also the head of a specific scientific research group focused on ‘High Productivity Data Processing’ and a cross-sectional team ‘Deep Learning’.

object detection 0

Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay

Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay Christian Bodenstein, Markus Goetz, Annika Jansen, Henrike Scholz, Morris Riedel In conference proceedings of the 15th IEEE International Conference on Machine Learning and Applications, IEEE ICMLA’16, Anaheim, USA, 18 Dec 2016 – 20 Dec 2016, ISBN 978-1-5090-6167-9, pp. 746 – 751, 2017 [ DOI ] [ Juelich ] Abstract: In this paper, we propose an instrumentation and computer vision pipeline that allows automatic object detection on images taken from multiple experimental set ups. We demonstrate the approach by autonomously counting intoxicated flies in the FLORIDA assay. The...

2016 Tutorial Einfuehrung Maschinelles Lernen

Tutorial: Einfuehrung in Maschinelles Lernen zur Datenanalyse (2016)

TUTORIAL: Einführung in Maschinelles Lernen zur Datenanalyse Invited Tutorial (German language) Smart Data Innovation Conference, Karlsruhe Institute of Technology (KIT), Germany 2016-10-13 [ Event ] Abstract: Der Kurs vermittelt Grundlagen zur Analyse von Daten und ist an Kursbesucher gerichtet die keine Vorkenntnisse in diesem Bereich haben. Die Inhalte werden prinzipielle Techniken umfassen, um Methoden der Datenanalyse wie Clustering, Klassifikation oder Regression besser einzuordnen. Das beinhaltet auch ein Verständnis von Testdaten, Trainingsdaten und Validierungsdaten. Anhand von einfachen Beispielen werden weiterhin Probleme wie bspw. overfitting angesprochen sowie dessen Lösungsansätze Validierung und Regularisierung. Nach dem Kurs haben Teilnehmer das Verständnis wie man an...

Scientific Big Data Analytics by HPC

Scientific Big Data Analytics by HPC

Scientific Big Data Analytics by HPC Thomas Lippert, Daniel Mallmann, Morris Riedel Publication Series of the John von Neumann Institute for Computing (NIC) NIC Series 48, 417, ISBN 978-3-95806-109-5, pp. 1 – 10, 2016 [ PID ] [ Juelich ] Abstract: Storing, managing, sharing, curating and especially analyzing huge amounts of data face an immense visibility and importance in industry and economy as well as in science and research. Industry and economy exploit ’Big Data’ for predictive analysis, to increase the efficiency of infrastructures, customer segmentation, and tailored services. In science, Big Data allows for addressing problems with complexities that...

image classification 0

On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods

On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods Gabriele Cavallaro, Morris Riedel, Matthias Richerzhagen, Jon Atli Benediktsson, Antonio Plaza IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Issue 99, pp. 1-13, 2015 [ DOI ] [ Juelich ] Abstract: Owing to the recent development of sensor resolutions onboard different Earth observation platforms, remote sensing is an important source of information for mapping and monitoring natural and man-made land covers. Of particular importance is the increasing amounts of available hyperspectral data originating from airborne and satellite sensors such as AVIRIS,...