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Machine Learning and Artificial Intelligence

01SQIOV, 02SQING

A.A. 2019/20

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Ingegneria Informatica (Computer Engineering) - Torino
Master of science-level of the Bologna process in Ingegneria Matematica - Torino

Course structure
Teaching Hours
Lezioni 54
Esercitazioni in laboratorio 6
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Tommasi Tatiana   Professore Associato ING-INF/05 27 0 0 0 2
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/05 6 D - A scelta dello studente A scelta dello studente
2018/19
The course, optional for the Master degree in computer engineering, is offered on the I semester of the II year. The course is taught in English. The course addresses the core issues in modern Artificial intelligence and machine learning, with a special focus on algorithms and theory of statistical machine learning, and modern techniques for deep learning. Lab activities will quip students with first-hand experience on modern optimization methods and programming framework most used in advance research and companies as of today, and to have first hand experiences on the properties of such algorithms on specific case studies.
The course, optional for the Master degree in computer engineering, is offered on the I semester of the II year. The course is taught in English. The course addresses the core issues in modern Artificial intelligence and machine learning, with a special focus on algorithms and theory of statistical machine learning, and modern techniques for deep learning. Lab activities will quip students with first-hand experience on modern optimization methods and programming framework most used in advance research and companies as of today, and to have first hand experiences on the properties of such algorithms on specific case studies.
- Knowledge of the main characteristics of artificial Intelligence: historical overview and modern definition; role of Machine and deep learning - Knowledge of the main characteristics of Statistical Machine Learning: theory and algorithms - Knowledge of the main characteristics of Artificial Neural Networks - Knowledge of the main characteristics of modern Deep Learning techniques
- Knowledge of the main characteristics of artificial Intelligence: historical overview and modern definition; role of Machine and deep learning - Knowledge of the main characteristics of Statistical Machine Learning: theory and algorithms - Knowledge of the main characteristics of Artificial Neural Networks - Knowledge of the main characteristics of modern Deep Learning techniques
Probability
Probability
Class topics - Artificial Intelligence: historical definition, brief overview, modern definition and current role of machine and deep learning. - Overview of fundamental knowledge of probability. - Generative and discriminative methods. - Perceptron. - Artificial Neural Networks. - Support Vector Machines. - Kernel functions. - Learning theory. - Convolutional Neural Networks. - Stochastic Gradiant Descent. - Batch Normalization. - Recurrent Neural Networks.
Class topics - Artificial Intelligence: historical definition, brief overview, modern definition and current role of machine and deep learning. - Overview of fundamental knowledge of probability. - Generative and discriminative methods. - Perceptron. - Artificial Neural Networks. - Support Vector Machines. - Kernel functions. - Learning theory. - Convolutional Neural Networks. - Stochastic Gradiant Descent. - Batch Normalization. - Recurrent Neural Networks.
I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT press. K. P. Murphy. Machine Learning: a probabilistic perspective. MIT Press
I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT press. K. P. Murphy. Machine Learning: a probabilistic perspective. MIT Press
Modalità di esame: Prova scritta (in aula); Prova orale obbligatoria;
Exam: Written test; Compulsory oral 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: Written test; Compulsory oral exam;
The exam includes a written part, which lasts 2 hours, a mandatory oral part, and the evaluation of the report on the individual practices assigned during the course.
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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