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PORTALE DELLA DIDATTICA

Mimetic learning

03QTIIU

A.A. 2019/20

Course Language

Inglese

Course degree

Doctorate Research in Ingegneria Informatica E Dei Sistemi - Torino

Course structure
Teaching Hours
Lezioni 20
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Squillero Giovanni Professore Associato ING-INF/05 20 0 0 0 8
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
*** N/A ***    
2018/19
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.
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.
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.
Le lezioni si terranno nella prima settimana di luglio presso l'ACSLAB dal 01/07 al 05/07 nell'orario 09:00-13:00.
Modalità di esame:
Exam:
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.
Exam:
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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