Explainable AI 4: Anomaly Detection and Explainability for Time Series

Date: 30 March 2027, 13:00 – 17:00 (CET)Price:  Free (for eligible participants)Format: AI:AT Webinar, ONLINE (Zoom)Target audience: Industry, public admin, academiaLanguage: EnglishOrganizer: AI:AT & ASCAs AI systems are increasingly used to monitor complex, time-dependent processes, understanding anomalies and their underlying causes becomes critical. This hands-on course introduces participants to anomaly detection and explainable AI (XAI) techniques for both transactional and time-series data, combining practical modeling with interpretable insights.This hands-on training introduces explainable AI (XAI) for anomaly detection in both tabular and time series data. Participants work with real-world datasets, including credit card fraud and NYC taxi demand, using standard machine learning models and SHAP-based explanations. The session covers the full workflow from data exploration to model interpretation. Special attention is given to challenges such as class imbalance and temporal dependencies. The course concludes with a critical discussion of limitations and best practices for applying XAI in practice for timeseries anomaly detection.Learning objectives:​Understand typical use cases of anomaly detection and explainability for transactional and time-series dataPerform exploratory data analysis and apply suitable anomaly detection techniquesBuild and evaluate models for anomaly detection in tabular and time-series settingsApply and interpret explainability methods (e.g., feature importance, SHAP)Recognize challenges such as class imbalance and temporal dependencies and account for them in practiceCritically assess the limitations and risks of explainability methods in real-world applicationsAgendaFor a detailed timetable and additional information, please see Agenda & Content in the left menu.Target audienceEveryone is welcome who wants to get a deeper understanding of how and why AI systems make decisions.This course is open and free of charge for all participants from academia, industry, and public administration from EU and/or EuroHPC JU member countries.Entry level & prerequisitesBasic – no prior XAI knowledge is required.Participants are expected to be familiar with basic machine learning concepts and python programming, prior hands-on XAI or anomaly detection experience is not required.Course formatThis course will be delivered as a LIVE ONLINE COURSE (using Zoom).Hands-on labsAll participants 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 conveniently from your browser to the ASC Jupyterhub and do the hands-on exercises on a suitable CPU or GPU partition of the ASC.Accepted participants will 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.LecturerAnahid Wachsenegger (AIT Austrian Institute of Technology GmbH)                    Anahid Wachsenegger is a data scientist at the AIT Austrian Institute of Technology, specializing in artificial intelligence, explainable machine learning, data-driven modeling, and time-series analysis across domains such as forestry and mobility data science. She holds a Master’s degree in Computational Intelligence from TU Wien and also served as an associate lecturer in Media and Digital Technologies at the University of Applied Sciences St. Pölten. She is passionate about developing trustworthy, transparent AI systems, applying data science to real-world challenges, and supporting students and practitioners in understanding and responsibly using modern AI technologies.LanguageEnglishDate, time & location30.03.2027, 13:00 – 17:00 (CET), LIVE ONLINE COURSE (Zoom)RegistrationRegistration is required, the link to the registration form can be found on the top of this page in the menu on the left. Please register with your official institutional email address to prove your affiliation.Please register with your official institutional email address to prove your affiliation.You will get an automatic confirmation by email (subject starting with "[Indico] Registration"), please check your Spam/Junk folders.Following your successful registration, you will receive further information a few days before the course.Please do not hesitate to contact us at [email protected] if you have any questions.WaitinglistAfter the number of registrations has reached its maximum or the registration form has been closed, you may want to send us an email ([email protected]) stating that you are interested to be put on the waiting list (vacancies may occur due to cancelations, etc.).To be able to do the hands-on labs on the ASC systems please provide your full international mobile-phone number for the two-factor authentication required to login to the ASC systems.Modification, withdrawal & no-show policyYour registration is binding. Please only register for the course if you are really going to attend.You can update your registration data or withdraw your registration anytime before the registration form has been closed via the link "Manage my registration" which you can find at the bottom of your automatic email confirmation (subject starting with "[Indico] Registration").Alternatively, or after the registration form has been closed, please inform us about your cancelation or any change in your registration data (especially your mobile-phone number) via email ([email protected]).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 Center, TU Wien).AcknowledgementsAI Factory Austria AI:AT has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101253078. The JU receives support from the Horizon Europe Programm of the European Union and Austria (BMIMI / FFG).

https://events.asc.ac.at/event/417/