Intelligent systems for industry, supply chain and environment

A.Y. 2020/2021
Overall hours
Learning objectives
The aim of this course is to introduce students to methodologies and techniques for intelligent systems for monitoring and control of industrial, environmental and supply chain applications, typically based on artificial intelligence techniques.
Expected learning outcomes
At the end of the course, the student will have acquired the ability to design, train, measure the performances and compare intelligent systems in order to achieve functionalities such as monitoring and control in industrial, environmental and supply chain applications.
Course syllabus and organization

Single session

Lesson period
Second semester
In case of emergency teaching conditions, asynchronous complete lessons will be available (video lessons consisting of the recording of the slides of the lesson with audio commentary). Asynchronous lessons will be available directly on the platform. At the established times of the course, it will be possible to have integrations or discussions in synchronous mode. The course delivered in asynchronous mode will in any case be complete. The program will not be modified.
In case of emergency teaching conditions, remote exams will be carried out using the MOODLE platform with SEB as illustrated on the University portal. The written test will have the same structure and similar duration to the normal test in presence.
Course syllabus
The course presents artificial intelligence techniques for the design, training and the effective deployment of intelligent systems in a wide range of applications.

After the description of the general design techniques for intelligent systems, ranging form the creation and management of the training dataset to the choice of the best models and training methods, the course presents different tools and environments for the implementation of the systems.

The course presents various examples with code to be tested on various environments.

Main topics covered:
· Artificial intelligence, intelligent systems and their application.
· Deep learning systems: design, training, validation and optimization.
· Advanced techniques and best practices. Data augmentation and transfer learning.
· Main environments for the creation of intelligent systems and deep learning (with specific examples using TensorFlow).
· Application of deep learning techniques for applications based on images.
· Application of clustering techniques and classification for the creation of intelligent systems.
· Automatic feature extraction from unstructured data, structured data and images.
· Information fusion.
· Intelligent systems for prediction.
· Application to ambient Intelligence.
· Application to signal and images: feature extraction, fusion of multi-sensor data. Study of industrial cases.
· Application to intelligent sensors and sensor networks. Acquisition and processing of sensor measurements. Study of environmental and ambient intelligence examples.
· Prediction, monitoring and control for applications for industry and the environment and the supply chain. Quality prediction.
· Applications of intelligent system for industrial processes, industrial automation, robotic systems, complex products, power distribution grids, automotive and transport systems and the supply chain.

A detailed list of topics of each lesson is presented and regularly updated on the course site.
Prerequisites for admission
Teaching methods
Frontal lessons
Teaching Resources
Course site:
Slides of the single lesson published on the course site
Handouts published on the course site
Assessment methods and Criteria
The exam is composed by a single compulsory test to be filled using the PCs in the laboratory requiring the solution of exercises and questions about the application and the design of intelligent systems, and multiple choice questions to verify the knowledge of theoretical notions of the same type and difficulty of the cases proposed and discussed during the curse. The grade is expressed in thirtieths.
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor: Scotti Fabio
By appointment (via email)
Computer Science Department, Via Celoria 18 - 20133 Milano (MI), Italy