1. Introduction: Machine learning, Industry 4.0, Knowledge discovery process 2. Supervised learning techniques and applications: predictive maintenance, predictive quality, image recognition 3. Unsupervised learning techniques and applications: product/customer clustering, outlier detection 4. Reinforcement learning techniques and applications: optimal path detection, nesting

No specific prerequisites

Data has become a highly valuable resource, even being cheap to be captured and stored. The diffusion of the Internet of Things (IoT) has allowed manufacturers to better manage productivity and efficiency on the shop floor. To further improve operations, manufacturers have turned to artificial intelligence and machine learning in order to leverage the massive amounts of data that is created during production. The aim of the course is introducing machine learning and how it can be actually useful in manufacturing applications. Each of the main ML techniques (i.e. supervised, unsupervised and reinforcement learning) is briefly described from the theoretical point of view, before presenting several applications in the manufacturing domain. Finally, some use cases will be implemented using Python language.

In presenza

Presentazione orale - Presentazione report scritto - Sviluppo di project work in team

P.D.1-1 - Dicembre

• Marted́ 16/01/24 dalle 9:00 alle 13:00 -> DIGEP B • Marted́ 23/01/24 dalle 9:00 alle 13:00 -> Aula 19 • Marted́ 30/01/24 dalle 9:00 alle 13:00 -> Aula 19 • Marted́ 06/02/24 dalle 9:00 alle 13:00 -> Aula 19