Machine Learning Techniques for Industrial Applications

Pervasive Technologies
Thesis Code: 
16021

Thesis Type: 6 months Master Thesis (Laurea Magistrale) for students of: Computer Engineering, Communications and ICT Engineering, Mathematical Engineering or equivalent.

Requirements:
- Skills in algorithms development and programming
- Interest for industrial applications.

Description:
Motivation:
The proposed work originates from two facts:
• Predictive control in industry could produce significant economical and environmental advantages. However, the industrial processes are generally very complex to model and control.
• Machine Learning has been proved to deal with complex systems and large datasets very efficiently. It is able to model input-output systems with very few model constraints.
It is then natural to think to the implementation of Machine Learning techniques to control and optimize industrial processes. Even though very popular for a variety of technological services, Machine Learning has also a great potential in the industrial domain, which has not been fully investigated yet. The interest on this topic is growing rapidly both from the industrial and scientific sectors.

Objective:
The purpose of this thesis is to study and develop Machine Learning algorithms and implement them to tackle industrial problems. A theoretical in-depth analysis of Machine Learning techniques will be the starting point of the work. Afterwards, the implementation of suitable algorithms will be tested on industrial datasets, with the purpose of exploiting time series data to predict failures and/or classify low-quality products.

Contacts: send a resume specifying the thesis code and title to pert@ismb.it.