Applications of AI for Anomaly Detection

Kód výsledku v 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>

Výsledek na webu

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

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Applications of AI for Anomaly Detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Applications of AI for Anomaly Detection

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

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

Projekt

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

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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