Applications of AI for Anomaly Detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F23%3A10252587" target="_blank" >RIV/61989100:27740/23:10252587 - isvavai.cz</a>

Result on the web

<a href="https://events.it4i.cz/event/159/" target="_blank" >https://events.it4i.cz/event/159/</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Applications of AI for Anomaly Detection

Original language description

Detecting anomalies is challenging as it is hard to retrieve labeled training data for supervised training. Anomalies are after all events that occur less likely and sporadically. They also can show a broad range of effects which not all can be covered during training.Different approaches are hence needed in order to train the machine and deep learning models for identifying situations that are rare and cannot be (fully) labeled.This course covered three different methods, using XGBoost, Autoencoders, and Generative Adversarial Networks (GANs). For each, detailed hands-on exercises were provided to learn how to use these methods and how to tackle the lack of labeled training data.

Czech name

—

Czech description

—

Type

O - Miscellaneous

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/MC2301" target="_blank" >MC2301: National Competence Centres in the framework of EuroHPC Phase 2 - EUROCC 2</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů