#Machine Learning

In Machine Learning, artificial systems learn from experience – similar to humans. This technology can be used in various applications in research and practice, for example to analyse medical data or fend off IT attacks.

Projects

Plant Monitoring AI

Leveraging machine learning and predictive analytics for early detection of plant stress for the benefit of sustainability in farming

Data Science Bootcamp

Training experts from within companies to become Data Scientists who are able to the best use of artificial intelligence methods

Publications

Horst, F., Slijepcevic, D., Lapuschkin, S., Raberger, A.-M., Zeppelzauer, M., Samek, W., … Horsak, B. (2020). On the Understanding and Interpretation of Machine Learning Predictions in Clinical Gait Analysis Using Explainable Artificial Intelligence. Frontiers in Bioengineering and Biotechnology, Submitted.
Horsak, B., Slijepcevic, D., Raberger, A.-M., Schwab, C., & Zeppelzauer, M. (2020). GaitRec, a large-scale walking GRF dataset for a healthy cohort and patients with musculo-skeletal impairments. Scientific Data, Submitted.
Slijepcevic, D., Zeppelzauer, M., Raberger, A.-M., Breitender, C., Horsak, B., & Horsak, Brian. (2020). Input Representations and Classification Strategies for Automated Human Gait Analysis. Gait & Posture, 76, 198–203.
Horsak, B., Dumphart, B., Slijepcevic, D., & Zeppelzauer, M. (2020). Explainable Artificial Intelligence (XAI) und ihre Anwendung auf Klassifikationsprobleme in der Ganganalyse. In Abstractband des 3. GAMMA Kongress. München, Deutschland.
Luh, Robert, & Schrittwieser, S. (2019). Advanced threat intelligence: detection and classification of anomalous behavior in system processes. E \& i Elektrotechnik Und Informationstechnik, Springer, 1–7.
Pirker, M. (2019, November). More Data - More Security? Invited Talk presented at the TOP Alumni Club, TU Wien.
Pirker, M. (2019, October). Digitale Probleme....für Alle! Presented at the PrivacyWeek, Wien.
Schrittwieser, S. (2019, September). Sicherheit von Container-Virtualisierung. Invited Talk presented at the IDC Security Roadshow Vienna, Wien.
Luh, R., Janicke, H., & Schrittwieser, S. (2019). AIDIS: Detecting and classifying anomalous behavior in ubiquitous kernel processes. Computers & Security, (84), 120–147. https://doi.org/https://doi.org/10.1016/j.cose.2019.03.015
Luh, R. (2019). Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes (Dissertation). De Monfort University Leicester.

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