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Language:Italian
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Textbooks:Notes from the course
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Learning objectives:Introduce the basic principles of machine learning.
EXPECTED LEARNING OUTCOMES
KNOWLEDGE AND UNDERSTANDING SKILLS:
At the end of the teaching the student will have to:
-) understand basics of machine learning;
-) understand the differences between the different basic principles;
-) know the most important models.
APPLIED KNOWLEDGE AND UNDERSTANDING:
At the end of the teaching the student should be able to:
-) understand if a certain problem can be solved with a certain technique;
-) formalize a problem;
-) operate in the field of machine learning.
COMMUNICATION SKILLS:
At the end of the teaching, the student should be able to expound on the concepts learned, using correct and precise language.
ABILITY TO LEARN:
At the end of the teaching the student should be able to read texts and research articles. -
Prerequisite:None.
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Teaching methods:Lectures.
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Exam type:Project.
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Sostenibilità:It does not address issues related to environmental sustainability.
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Further information:E-mail: parton@unich.it.
Mobile phone: 349-5323-199.
-) Introduction to machine learning;
-) introduction to neural networks;
-) introduction to reinforcement learning;
-) applications;
-) introduction to Python+Keras;
-) implementation of the applications in Keras.
-) Introduction to machine learning;
-) introduction to neural networks;
-) introduction to reinforcement learning;
-) applications;
-) introduction to Python+Keras;
-) implementation of the applications in Keras.
Scopri cosa vuol dire essere dell'Ud'A
SEDE DI CHIETI
Via dei Vestini,31
Centralino 0871.3551
SEDE DI PESCARA
Viale Pindaro,42
Centralino 085.45371
email: info@unich.it
PEC: ateneo@pec.unich.it
Partita IVA 01335970693
