The goal of this module is to give participants a better insight into the type of problems that can be tackled using machine-learning techniques, illustrated by several real-world examples from a diverse set of domains.
Among others, the following machine-learning methodologies will be covered:
- Reinforcement learning
- Deep learning
- Bio-inspired computing
- Active learning
- Federated learning
For each method, its characteristics, advantages, and disadvantages will be explained in more detail, as well as the most commonly used algorithm(s) to solve a particular industrial problem. The aim is to guide the participants in making a conscious choice for the appropriate technique in function of the problem setting at hand, as well as the available data (dimensionality, attribute types, etc.) and the expected model requirements (interpretability, accuracy, scalability, etc.).
The working language of this module is English.
Sirris, MRC Gent, Technologiepark 48, 9052 Zwijnaarde, Belgium
Cost per module: 575 EUR (exclusive of VAT)
Thanks to the support of VLAIO (Industriepartnerschap), technology companies located in Flanders pay only 575 euro for 2 modules, if they book and follow 2 modules. This cannot be cumulated with the kmo-portefeuille.
Other module organized this year for which the VLAIO reduction (Industriepartnerschap) applies:
If you are a Flemish SME you can also make use of the kmo-portefeuille (the kmo-portefeuille should be requested at latest 14 days after the seminar has taken place - if not, your application will be refused). (Sirris accreditation no: DV.O105154). The kmo-portefeuille cannot be cumulated with the VLAIO Industriepartnerschap reductions.
- Invoices will be sent after the event. You can consult our general conditions on our website.
- If you are unable to attend, you can be replaced by a colleague. Please notify us by email to email@example.com of the name of your colleague.
- Cancellations must be made by email to firstname.lastname@example.org. You can cancel your participation free of charge up to 3 working days before the event. In case of cancellation after this date or non-participance without cancellation, you will be invoiced for the full participation fee.