Prof. Dr. - Ing. Morris Riedel

2018-06-06-Introduction-To-Deep-Learning-Tutorial-Content 0

Talks

DEEP-EST Tutorial: Introduction to Deep Learning

DEEP-EST Tutorial: Introduction to Deep Learning Tutorial under the umbrella of the DEEP-EST EU Project Juelich Supercomputing Centre, Germany 2018-06-06 – 2018-06-07 [ Event ] Materials [ Lecture 1 – Introduction to Deep Learning – Slides ~3.29 MB (pdf) ] [ Lecture 2 – Fundamentals of Convolutional Neural Networks (CNNs) – Slides ~3.81 MB (pdf) ] [ Lecture 3 – Deep Learning in Remote Sensing: Challenges – Slides ~8.11 MB (pdf) ] [ Lecture 4 – Deep Learning in Remote Sensing: Applications – Slides ~1.98 MB (pdf) ] [ Lecture 5 – Model Selection and Regularization – Slides ~1.78 MB...

Morris Riedel Research 0

Research

Research – Overview

Research Areas Research of the high productivity data processing research group at Forschungszentrum Juelich – Juelich Supercomputing Centre and University of Iceland focuses on application-driven parallel and scalable machine learning methods including related areas such as feature engineering, statistical data mining, and innovative deep learning techniques. In our projects we are cooperating with academic research groups worldside as well as with selected industry companies including the support of start-ups. Additional funding sources are the European Union or the German Federal Ministry of Education and Research. If you are interested in one of these research topics please contact me by email...

Morris Riedel Talks

Talks

Talks – Overview

2018 Modular Supercomputing Design supporting Machine Learning Applications IEEE MIPRO Conference 2018 Opatija, Croatia 2018-05-24 [ More ] SIMDAS & Industry Relations – Examples from Juelich Invited Talk Simulation and Data Science Center of Excellence (SIMDAS) The Cyprus Institute, Nicosia, Cyprus 2018-05-03 [ More ] Deep Learning with Python Invited Talk Joint Laboratory for Extreme Scale Computing Barcelona Supercomputing Centre, Barcelona, Spain 2018-04-19 [ More ] Selected Comparisons between Machine Learning and Deep Learning in Earth Science Applications Invited Talk International Conference on: Terrestrial Systems Research: Monitoring, Prediction & High Performance Computing University of Bonn, Germany 2018-04-05 [ More ]...

Morris Riedel Theses 0

Theses

Theses – Overview

Theses Proposals I’m always open to proposals for bachelor, master, or doctoral thesis topics that are along the lines of my research interests. Feel free to contact me for details. Doctoral Theses [In Progress] Deep Learning Application Co-Design on Innovative HPC Architectures Ernir Erlingsson, Doctoral Thesis University of Iceland, School of Engineering and Natural Sciences (SENS), Iceland [In Progress] Design and Evaluation of a Joint Model of Service Access including Service Discovery and Federated Identity Management in e-infrastructures Ahmed Shiraz Memon, Doctoral Thesis University of Iceland, School of Engineering and Natural Sciences (SENS), Iceland [In Progress] Automated Data Processing and...

Morris Riedel Teaching 0

Teaching

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 [ More ] Cloud Computing and Big Data 18 university lectures with additional practical lectures for hands-on exercises in context University of Iceland, School of...

Morris Riedel Media 0

Media

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

Publications – Overview

2018 Automatic Water Mixing Event Identification in the Koljö Fjord Observatory Data Markus Goetz, Mikhail Kononets, Christian Bodenstein, Morris Riedel, Matthias Book, Olafur Petur Palsson International Journal of Data Science and Analytics, Springer, DOI: 10.1007/s41060-018-0132-z, 2018 [ Journal ] The Influence of Sampling Methods on Pixel-Wise Hyperspectral Image Classification with 3D Convolutional Neural Networks Julius Lange, Gabriele Cavallaro, Markus Goetz, Ernir Erlingsson, Morris Riedel In conference proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2018, Valencia, Spain, to appear [ Event ] Scaling Support Vector Machines Towards Exascale Computing for Classification of Large-Scale High-Resolution Remote Sensing Images...

Deep Learning with Python 0

Talks

Deep Learning with Python

Deep Learning with Python Invited Talk Joint Laboratory for Extreme-Scale Computing (JLESC) 8th Workshop Barcelona Supercomputing Centre, Barcelona, Spain 2018-04-19 [ Event ] [ Slides ~6.53 MB (pdf) ]

2018-03-06-Parallel-and-Scalable-Machine-Learning-Tutorial-Content 0

Talks

DEEP-EST Tutorial: Parallel and Scalable Machine Learning

DEEP-EST Tutorial: Parallel and Scalable Machine Learning Tutorial under the umbrella of the DEEP-EST EU Project Juelich Supercomputing Centre, Germany 2018-03-06 – 2018-03-08 [ 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...