OVERVIEW

Soft Computing introduces a set of Artificial Intelligence computational techniques which are based on the emulation of biological processes.

In particular, we will introduce neural networks mimicking human brain skills to learn and generalize, evolutionary computation mimicking adaptation of biological species to their own environment, and swarm intelligence based on the model of intelligent cooperative behavior of some animals. A specific computer exercise, in MATLAB environment, is foreseen for each topic.

The lectures will be held in person.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims to describe the main numerical computation techniques based on the emulation of biological processes. The goal is to provide students with modern computational tools within the Artificial Intelligence domain such as neural networks with and without memory, self-organizing maps, genetic algorithms, evolutionary computation, and swarm intelligence.

AIMS AND LEARNING OUTCOMES

The general objective of the module is to provide students with computational techniques, based on the emulation of successfull biological processes, for the solution of classification, clustering, optimization and forecasting problems. In particular, specific objectives are aimed at the acquisition of knowledge and skills in the field of:

• Supervised and unsupervised learning techniques for classification, clustering and prediction problems (neural networks);
• Global optimization techniques (evolutionary computing, swarm intelligence)

Specifically, upon completion of the course, students will know how to:

• Design and implement the architecture of single-layer and multilayer neural networks;
• Implement genetic algorithms and compare their performance under varying selection, reproduction and mutation strategies adopted;
• Solve unsupervised clustering problems following fuzzy approaches and infer a posteriori information characterizing the identified classes;
• Implement the swarm intelligence algorithm ACO (Ant Colony Optimization).

PREREQUISITES

All topics are addressed in a self-consistent manner

TEACHING METHODS

The module includes lectures and three computer exercises

SYLLABUS/CONTENT

Neural networks

• Introduction
• Single- and Multi-layer perceptron
• Recurrent Neural Networks
• Self-Organizing Maps

Evolutionary computation

• Introduction
• Genetic algorithms
• Evolutionary algorithms

Swarm Intelligence

• Particle Swarm Optimization
• Ant Colony Optimization

RECOMMENDED READING/BIBLIOGRAPHY

Lectures notes will be provided

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

In accordance with the academic calendar approved  by the Consiglio di Corso di Studi.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Three computer exercises will take place for which deadlines will be set. Positive assessment will be required for admission to a final oral exam

ASSESSMENT METHODS

Computer exercises are aimed at testing the practical skills acquired for the solution of the posed problems. They will be evaluated on the basis of the following criteria:

• accuracy and optimization of the code
• accuracy and presentation of the results (images, graphs, tables ...)
• comments on the procedures followed and on the results obtained

The oral exam is finally aimed at assessing the ability to communicate the knowledge acquired in a clear and competent manner

FURTHER INFORMATION

Students with DSA certification ("specific learning disabilities"), disability or other special educational needs are advised to contact the teacher at the beginning of the course to agree on teaching and examination methods that, in compliance with the teaching objectives, take account of individual learning arrangements and provide appropriate compensatory tools.