BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CERN//INDICO//EN BEGIN:VEVENT SUMMARY:Predictive Maintenance with C-MAPSS DTSTART:20261111T120000Z DTEND:20261111T153000Z DTSTAMP:20261102T121200Z UID:indico-event-349@events.asc.ac.at CONTACT:training@ai-at.eu DESCRIPTION:Date:  11 November 2026\, 13:00-16:30 (CET)  Entry level:   BasicLocation:  ONLINE (Zoom)    Topics:  Explainable AI (XAI)Language :  English    Target audience:  Industry\, public admin\, academiaPric e:  Free (for eligible participants)    Organizers:  AI:AT & ASCAs Art ificial Intelligence (AI) becomes increasingly woven into our daily lives\ , the need to understand how and why these systems make decisions has neve r been more important.This intensive half-day course provides a practical\ , hands-on introduction to Predictive Maintenance using multivariate time- series data. Utilizing the industry-standard NASA C-MAPSS dataset\, partic ipants will learn the end-to-end process of identifying equipment degradat ion\, predicting Remaining Useful Life (RUL)\, and explaining machine lear ning decisions to domain experts.Key Learning ObjectivesDegradation Detect ion: Understand run-to-failure trajectories\, analyze sensor signals acros s various operating conditions\, and identify anomalous behavior in comple x machinery.Remaining Useful Life (RUL) Prediction: Learn how to construct an RUL target from time-series data\, apply feature- and window-based mod eling techniques\, and evaluate model performance and error rates.Explaina ble AI (XAI) in Maintenance: Use SHAP and feature-attribution techniques t o interpret model outputs\, determining exactly which sensor characteristi cs drive specific RUL predictions across healthy\, degrading\, and near-fa ilure states.Agenda & ContentFor a detailed timetable and additional infor mation\, please see Agenda & Content in the left menu.Target audience\, el igibility & pricesAI practitioners\, data scientists\, engineers\, researc hers\, and students interested in predictive maintenance\, time-series ana lysis\, and trustworthy machine learning. The course is especially relevan t for participants working with industrial data or looking to apply AI to real-world maintenance problems.This course is open and free of charge for all participants from academia\, industry\, and public administration fro m EU and/or EuroHPC JU member countries.Entry level & prerequisitesBasic k nowledge of machine learning and Python is recommended. Familiarity with s upervised learning concepts is helpful\, but no prior experience with pred ictive maintenance or explainable AI is required.Course formatThis course will be delivered as a LIVE ONLINE COURSE (Zoom).Hands-on labsAll particip ants will get a temporary user account on one of the ASC systems to do the hands-on labs.You will use your own laptop or workstation to connect conv eniently from your browser to the ASC Jupyterhub and do the hands-on exerc ises on a suitable CPU or GPU partition of the ASC.Accepted participants w ill be contacted a few days before the course and asked to do a short pre- assignment that has to be completed before the course starts.LecturerAnahi d Wachsenegger (AIT Austrian Institute of Technology GmbH)                     Anahid Wachsenegger is a data scientist at the AIT Austrian Institute of Technology\, specializing in artificial intelligenc e\, explainable machine learning\, data-driven modeling\, and time-series analysis across domains such as forestry and mobility data science. She ho lds a Master’s degree in Computational Intelligence from TU Wien and als o served as an associate lecturer in Media and Digital Technologies at the University of Applied Sciences St. Pölten. She is passionate about devel oping trustworthy\, transparent AI systems\, applying data science to real -world challenges\, and supporting students and practitioners in understan ding and responsibly using modern AI technologies.LanguageEnglishDate\, ti me\, and location11.11.2026\, 13:00 – 16:30 (CET)\, LIVE ONLINE COURSE ( Zoom)RegistrationRegistration is required\, the registration form can be f ound on top of this page in the menu on the left.Please register with your official institutional email address to prove your affiliation.You will g et an automatic confirmation by email (subject starting with "[Indico] Reg istration")\, please check your Spam/Junk folders.Following your successfu l registration\, you will receive further information a few days before th e course.Please do not hesitate to contact us at training@ai-at.eu if you have any questions.WaitinglistAfter the number of registrations has reache d its maximum or the registration form has been closed\, you may want to s end us an email (training@ai-at.eu) stating that you are interested to be put on the waiting list (vacancies may occur due to cancelations\, etc.).T o be able to do the hands-on labs on the ASC systems please provide your f ull international mobile-phone number for the two-factor authentication re quired to login to the ASC systems.Modification\, withdrawal & no-show pol icyYour registration is binding. Please only register for the course if yo u are really going to attend.You can update your registration data or with draw your registration anytime before the registration form has been close d via the link "Manage my registration" which you can find at the bottom o f your automatic email confirmation (subject starting with "[Indico] Regis tration").Alternatively\, or after the registration form has been closed\, please inform us about your cancelation or any change in your registratio n data (especially your mobile-phone number) via email (training@ai-at.eu) .No-show policy: If you do not cancel and do not show up at the course you will be blacklisted and excluded from future training events.Organizers                This course is jointly organized by AI Factory Austria AI:AT and Austrian Scientific Computing ASC (aka ASC Research Cent er\, TU Wien).AcknowledgementsAI Factory Austria AI:AT has received fundin g from the European High-Performance Computing Joint Undertaking (JU) unde r grant agreement No 101253078. The JU receives support from the Horizon E urope Programm of the European Union and Austria (BMIMI / FFG).\n\nhttps:/ /events.asc.ac.at/event/349/ LOCATION:Zoom (ONLINE) URL:https://events.asc.ac.at/event/349/ END:VEVENT END:VCALENDAR