Learning outcomes of the course unit
The course aims at providing students with the basic concepts regarding adaptive methods, often biologically-inspired, which allow computer emulation of learning-from-examples processes, and are used to optimize/design solutions to real-world problems.
Course contents summary
Biological and automatic learning.
Review of the main classical machine-learning techniques
Soft Computing techniques
Examples of real-world applications
Practical assignments in laboratory on real-world examples
Teaching material available online and other material that will be distributed and published online all through the course.
Tettamanzi Tomassini - Soft computing : integrating evolutionary, neural, and fuzzy systems. Springer, 2001
Haykin - Neural Networks. US Imports & PHIPEs, 1998
Eiben - Smith, Introduction to Evolutionary Computing, Springer, 2003
Banzhaf Nordin Keller Francone - Genetic Programming, Morgan Kaufmann, 1998
Intermediate evaluation of the lab assignments and final project