Data Science Liège MEETUP #12 - Online
Sidebar
Date
25.06.2020
À partir de
17:00
Lieu
A link to the meetup will be sent after registration.
The mission of Data Science Liège is to offer a forum, upon which participants can leverage to federate data science initiatives, showcase projects and ideas, call for support and partnerships, disseminate knowledge and stimulate public awareness.
In order to comply with the measures taken by our government in the struggle against Covid-19, our 12th meetup will be held online.
Participation is free but registration is required.
Follow us on Twitter : @DSLiege for updates and latest news!
PS: We are looking for participants to present and showcase their past or ongoing data science projects. Feel free to submit your propositions by email to datascience@uliege.be.
Programme
- 17:00 Introduction
- 17:05 Short Talks + Q&A
- 18:00 End of the meetup
Intervenants
-
Samy DOLORIS
Data Scientist - NRB -
Fabien FRANCIS
Data Scientist - Micropole
Abstract
Furniture detection and price estimation using machine learning – Samy DOLORIS
In the era of digitization, machine learning can greatly help to enhance insurances products and services. One of the main business values that machine learning can bring is user-experience.
Our use-case focuses on helping rental insurance customers to encode their furniture using machine learning and computer vision.
This solution detects furniture, using the deep learning model YOLOv3, and matches it to a price table, allowing to estimate the price of the insuree’s goods.
Doing so can make the insurance product more customizable and adapted to the needs of the customer.
Tremplin vers l’Industrie 4.0 : le Machine Learning au service de la qualité dans la métallurgie – Fabien FRANCIS
L’IoT industriel (IIoT) combiné au Machine Learning et au Cloud computing offrent de nouvelles perspectives aux industries wallonnes : faire de la maintenance prédictive permettant d’identifier les éventuelles pannes avant qu’elles n’affectent la production, contrôler et détecter en temps réel d’éventuelles anomalies pour optimiser la performance, ou faire des prédictions de la qualité de produits. Pour illustrer ces innovations, nous aborderons l’exemple de d’un bureau d’études spécialisé dans les fours de traitement thermique pour produits métalliques et leader dans son secteur.