AIMS AND CONTENT

TEACHING METHODS

Class and outside preparation

SYLLABUS/CONTENT

• Neural Networks (notes)
• Ensemble of learning machines
• Evolutionary Algorithms (Genetic Algorithms, Evolutionary Stategies, Genetic Programming)
• Particle Swarm Optimization
• Simulated Annealing
• Fuzzy sets – Approximate reasoning
• Fuzzy systems
• Neuro-Fuzzy Systems
• Applications to  combinatorial optimization and to machine learning (in particular to clustering)
• Case studies in bioinformatics, ambient assisted living, activity analysis and recognition and emotion recognition.

RECOMMENDED READING/BIBLIOGRAPHY

D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, 1989 (MAT 68-1989-48IN, CHI M.9, ING1 A.ELE.T.0264)
S. O. Haykin, Neural Networks and Learning Machines, Prentice Hall, 2009
H.J. Zimmermann, Fuzzy set theory and its applications, 2ed., Kluwer Academic Publishers, 1996 (MAT 04-1996-01, MAT 04-1996-02, LETT 14.E.169)

TEACHERS AND EXAM BOARD

LESSONS

EXAMS

EXAM DESCRIPTION

Oral examination and project discussion