Period: JULY The course illustrates several heuristic methodologies able to tackle complex problems. While the techniques differ in many respects, they all share an attempt to learn the optimal strategy by mimicking natural processes.

The course introduces the vast family of algorithms that have been labeled either “machine learning” or “evolutionary algorithms”, and puts them into an historical perspective. Differences and correlations among the different approaches are shown, together with the backgrounds and the breakthroughs that led to the current “Deep learning” hype. In more details: • Introduction to (Evolutionary) Machine Learning and Computational Intelligence o Taxonomy: Classification, regression, clustering o Representation Learning o Deep learning o Included a crash course on Python/Jupyter and other tools used in data science • Evolutionary algorithms and Evolutionary Machine Learning o Classic paradigms (Genetic Algorithm, Genetic Programming, Evolution Strategies, and Evolutionary Programming) o Particle-Swarm Optimization and Ant-Colony Optimization o Differential Evolution o Estimation of Distribution Algorithm o Artificial Immune Systems • Rule-Based Systems o Learning Classifier Systems Mimetic Learning belongs to an educational path on Data Science. The path is composed by an introductory course covering data analytics fundamentals, which is a cultural prerequisite for the other courses (Data Mining: Concepts and Algorithms), and other four thematic courses dealing in depth with specific topics, algorithm types and application domains: Data Analytics for Science and Society; Machine Learning for Pattern Recognition; Text Mining and Analytics; Visualization and Visual Analytics.

Le lezioni si terranno nella prima settimana di luglio presso l'ACSLAB dal 01/07 al 05/07 nell'orario 09:00-13:00.

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Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.