[mooc] Artificial Intelligence und Machine Learning #ai #ingo #imoox

Die Universität Linz zusammen mit der Technischen Universität Wien hat einen neuen MOOC zu “Artificial Intelligence und Machine Learning” gestartet im Rahmen Ihrer MOOC-Serie INGO. Danke für die tollen Inhalte für alle Informatik-Begeisterten:

In diesem Kurs werden wir uns mit den Begriffen der Künstlichen Intelligenz und Machine Learning auseinandersetzen und etwas tiefer in die Materie einsteigen. Wir beginnen mit einer Einführung in die Thematik und erläutern, was sich hinter den einzelnen Begriffen Artificial Intelligence, Machine Learning und Deep Learning versteckt, und worin sie sich unterscheiden. Du wirst alle notwendigen Grundlagen im Bereich Datenverarbeitung, Machine Learning und Evaluierung lernen. Außerdem erläutern wir dir eine Liste an “intelligenten” Algorithmen für verschiedene Problemstellungen.
Im Zuge dieses Kurses wirst du deine eigenen Machine Learning Projekte umsetzen, beginnend bei der Datenanalyse und -verarbeitung bis hin zur Evaluierung deines eigenen Machine Learning Modells. 
Bist du immer noch interessiert oder denkst dir “warum eigentlich nicht”? Dann starte den Kurs und los geht’s!

[Link zur kostenlosen Registrierung]

[publication] Property-Based Testing for Parameter Learning of Probabilistic Graphical Models #machinelearning #learninganalytics

Thanks to my colleagues – we did a publication for this year CDMAKE-conference about “Property-Based Testing for Parameter Learning of Probabilistic Graphical Models“.

Code quality is a requirement for successful and sustainable software development. The emergence of Artificial Intelligence and data driven Machine Learning in current applications makes customized solutions for both data as well as code quality a requirement. The diversity and the stochastic nature of Machine Learning algorithms require different test methods, each of which is suitable for a particular method. Conventional unit tests in test-automation environments provide the common, well-studied approach to tackle code quality issues, but Machine Learning applications pose new challenges and have different requirements, mostly as far the numerical computations are concerned. In this research work, a concrete use of property-based testing for quality assurance in the parameter learning algorithm of a probabilistic graphical model is described. The necessity and effectiveness of this method in comparison to unit tests is analyzed with concrete code examples for enhanced retraceability and interpretability, thus highly relevant for what is called explainable AI.

abstract of the article

[publication @ book homepage]
[draft @ ResearchGate]

Reference: Saranti A., Taraghi B., Ebner M., Holzinger A. (2020) Property-Based Testing for Parameter Learning of Probabilistic Graphical Models. In: Holzinger A., Kieseberg P., Tjoa A., Weippl E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2020. Lecture Notes in Computer Science, vol 12279. Springer, Cham. https://doi.org/10.1007/978-3-030-57321-8_28

[publication] Insights into Learning Competence Through Probabilistic Graphical Models #tugraz #research

We contributed to this year “Third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference (CD-MAKE 2019)” with a publication titled “Insights into Learning Competence Through Probabilistic Graphical Models“.


One-digit multiplication problems is one of the major fields in learning mathematics at the level of primary school that has been studied over and over. However, the majority of related work is focusing on descriptive statistics on data from multiple surveys. The goal of our research is to gain insights into multiplication misconceptions by applying machine learning techniques. To reach this goal, we trained a probabilistic graphical model of the students’ misconceptions from data of an application for learning multiplication. The use of this model facilitates the exploration of insights into human learning competence and the personalization of tutoring according to individual learner’s knowledge states. The detection of all relevant causal factors of the erroneous students answers as well as their corresponding relative weight is a valuable insight for teachers. Furthermore, the similarity between different multiplication problems – according to the students behavior – is quantified and used for their grouping into clusters. Overall, the proposed model facilitates real-time learning insights that lead to more informed decisions.

[Proceedings online @ Springer]

[Draft @ ResearchGate]

Reference: Saranti, A., Taraghi, B., Ebner, M., Holzinger, A. (2019) Insights into Learning Competence Through Probabilistic Graphical Models. In: Machine Learning and Knowledge Extraction. Third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2019, Canterbury, UK, August 26–29, 2019, Proceedings. pp. 250-271