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SUMMARY:Anomaly Detection and Explainability for Time-Series Data
DTSTART:20260622T110000Z
DTEND:20260622T143000Z
DTSTAMP:20260616T202000Z
UID:indico-event-291@events.asc.ac.at
CONTACT:training@ai-at.eu
DESCRIPTION:Start Date: 22 June 2026\, 13:00 (CEST) Entry level:  Basic
 End date:  22 June 2026\, 16:30 (CEST) Subject area:  Artificial Intell
 igence (AI)Location:  Online Topics:  Anomaly Detection\, Time-SeriesLa
 nguage: English   Target audience:  Industry\, public admin\, academia
 Price:  Free (for eligible participants)   Organizers:  AI:AT & ASCAs 
 AI systems are increasingly used to monitor complex\, time-dependent proce
 sses\, understanding anomalies and their underlying causes becomes critica
 l. This hands-on course introduces participants to anomaly detection and e
 xplainable 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 tab
 ular and time series data. Participants work with real-world datasets\, in
 cluding credit card fraud and NYC taxi demand\, using standard machine lea
 rning models and SHAP-based explanations. The session covers the full work
 flow from data exploration to model interpretation. Special attention is g
 iven to challenges such as class imbalance and temporal dependencies. The 
 course concludes with a critical discussion of limitations and best practi
 ces for applying xAI in practice for timeseries anomaly detection.Learning
  Outcomes: After attending this course\, you should be able toUnderstand t
 ypical 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 detectio
 n in tabular and time-series settingsApply and interpret explainability me
 thods (e.g.\, feature importance\, SHAP)Recognize challenges such as class
  imbalance and temporal dependencies and account for them in practiceCriti
 cally assess the limitations and risks of explainability methods in real-w
 orld applications For a detailed timetable and additional information\, p
 lease see Agenda & Content in the left menu.Target audience\, eligibility 
 & pricesEveryone 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 & prerequisitesBasi
 c – no prior XAI knowledge is required.Participants are expected to be f
 amiliar with basic machine learning concepts and python programming\, prio
 r hands-on xAI or anomaly detection experience is not required.Course form
 atThis 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 A
 SC systems to do the hands-on labs.You will use your own laptop or worksta
 tion to connect conveniently from your browser to the ASC Jupyterhub and d
 o the hands-on exercises on a suitable CPU or GPU partition of the ASC.Acc
 epted participants will be contacted a few days before the course and aske
 d to do a short pre-assignment that has to be completed before the course 
 starts.LecturerAnahid Wachsenegger (AIT Austrian Institute of Technology G
 mbH)                       Anahid Wachsenegger is a
  data scientist at the AIT Austrian Institute of Technology\, specializing
  in artificial intelligence\, explainable machine learning\, data-driven m
 odeling\, and time-series analysis across domains such as forestry and mob
 ility data science. She holds a Master’s degree in Computational Intelli
 gence from TU Wien and also served as an associate lecturer in Media and D
 igital Technologies at the University of Applied Sciences St. Pölten. She
  is passionate about developing trustworthy\, transparent AI systems\, app
 lying data science to real-world challenges\, and supporting students and 
 practitioners in understanding and responsibly using modern AI technologie
 s.LanguageEnglishDate\, time\, and location22.04.2026\, 13:00 – 16:30 (C
 EST)\, LIVE ONLINE COURSE (Zoom)RegistrationRegistration is required\, the
  registration form can be found on top of this page in the menu on the lef
 t.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 folde
 rs.Following your successful registration\, you will receive further infor
 mation a few days before the course.Please do not hesitate to contact us a
 t training@ai-at.eu 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 (training@ai-at.eu) stating tha
 t you are interested to be put on the waiting list (vacancies may occur du
 e to cancelations\, etc.).To be able to do the hands-on labs on the ASC sy
 stems please provide your full international mobile-phone number for the t
 wo-factor authentication required to login to the ASC systems.Modification
 \, withdrawal & no-show policyYour registration is binding. Please only re
 gister for the course if you are really going to attend.You can update you
 r registration data or withdraw your registration anytime before the regis
 tration form has been closed via the link "Manage my registration" which y
 ou can find at the bottom of your automatic email confirmation (subject st
 arting with "[Indico] Registration").Alternatively\, or after the registra
 tion form has been closed\, please inform us about your cancelation or any
  change in your registration data (especially your mobile-phone number) vi
 a email (training@ai-at.eu).No-show policy: If you do not cancel and do no
 t show up at the course you will be blacklisted and excluded from future t
 raining events.Organizers                This course is jointl
 y 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 J
 oint Undertaking (JU) under grant agreement No 101253078. The JU receives 
 support from the Horizon Europe Programm of the European Union and Austria
  (BMIMI / FFG). \n\nhttp://events.asc.ac.at/event/291/
LOCATION:Zoom (ONLINE)
URL:http://events.asc.ac.at/event/291/
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