Prof. Dr. - Ing. Morris Riedel

2020-01-20 Artificial Intelligence Data Analysis School for Heliophysicists Morris Riedel 0

Teaching

Artificial Intelligence Data Analysis School for Heliophysicists

Artificial Intelligence Data Analysis School for Heliophysicists Invited Training Course organized by the EC Project Artificial Intelligence Data Analysis (AIDA) CINECA, Bologna, Italy 2020-01-20 – 2020-01-22 [ EVENT ] Materials [ Lecture 1: Introduction and Differences between AI, ML, NN, and Big Data – Slides ~10.2 MB (pdf) ] [ Lecture 2: Unsupervised Learning – Clustering – Slides ~7.08 MB (pdf) ] [ Lecture 3: Unsupervised Learning – Computing – Slides ~10.2 MB (pdf) ] [ Lecture 4: Supervised Learning – Multi-Class Classification and Generalization – Slides ~8.99 MB (pdf) ] [ Lecture 5: Supervised Learning – Artificial Neural Networks...

2018 Tutorial Parallel and Scalable Machine Learning 0

Teaching

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...

2016 Tutorial Einfuehrung Maschinelles Lernen

Teaching

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...