PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

Elenco notifiche



Machine learning in manufacturing applications

01HXFUQ

A.A. 2024/25

Course Language

Inglese

Degree programme(s)

Doctorate Research in Ingegneria Gestionale E Della Produzione - Torino

Course structure
Teaching Hours
Lezioni 16
Lecturers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Bruno Giulia   Professore Associato IIND-04/A 16 0 0 0 2
Co-lectures
Espandi

Context
SSD CFU Activities Area context
*** N/A ***    
1. Introduction: Industry 4.0 and 5.0, Data-driven manufacturing, Knowledge discovery process, Data mining vs. Machine learning 2. Supervised learning: applications in manufacturing (e.g., predictive maintenance, product quality), regression algorithms, classification algorithms, deep learning, coding examples in Python 3. Unsupervised learning: applications in manufacturing (e.g., product/customer clustering, outlier detection), clustering algorithms, outlier detection algorithms, coding examples in Python 4. Reinforcement learning: manufacturing applications (e.g., robot path detection, nesting), reinforcement learning algorithms, coding examples in Python
1. Introduction: Industry 4.0 and 5.0, Data-driven manufacturing, Knowledge discovery process, Data mining vs. Machine learning 2. Supervised learning: applications in manufacturing (e.g., predictive maintenance, product quality), regression algorithms, classification algorithms, deep learning, coding examples in Python 3. Unsupervised learning: applications in manufacturing (e.g., product/customer clustering, outlier detection), clustering algorithms, outlier detection algorithms, coding examples in Python 4. Reinforcement learning: manufacturing applications (e.g., robot path detection, nesting), reinforcement learning algorithms, coding examples in Python
No specific prerequisites
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 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 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.
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 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 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
On site
Presentazione orale - Presentazione report scritto - Sviluppo di project work in team
Oral presentation - Written report presentation - Team project work development
P.D.1-1 - Gennaio
P.D.1-1 - January
Lessons take place from 9.00 to 13.00 accordingly to the following schedule: - 16/01 room DIGEP B, - 23/01 room 6I, - 30/01 room 6I, - 6/02 room 6I. Here the locations fo room DIGEP B (https://www.polito.it/mappe?bl_id=TO_CEN01&fl_id=XPTE&rm_id=B042) and room 6I (https://www.polito.it/mappe?bl_id=TO_CIT08&fl_id=XS01&rm_id=007).