PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

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Deep learning (didattica di eccellenza)

01TEVRV

A.A. 2018/19

Course Language

Inglese

Degree programme(s)

Doctorate Research in Ingegneria Elettrica, Elettronica E Delle Comunicazioni - Torino

Course structure
Teaching Hours
Lezioni 30
Lecturers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Pasero Eros Gian Alessandro Professore Associato IINF-01/A 2 0 0 0 1
Co-lectures
Espandi

Context
SSD CFU Activities Area context
*** N/A ***    
2018/19
PERIOD: JANUARY - MARCH Prof. Giansalvo CIRRINCIONE - Université de Picardie This course is the natural continuation of the lessons on "neural networks and pattern recognition" and “unsupervised neural networks”, in the sense that it completes the cycle on neural networks. However, it is self-contained, i.e. it does not require any knowledge about neural networks. It can be useful for students learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research. The other target audience is software engineers who do not have a machine learning or statistics background but want to rapidly acquire one and begin using deep learning in their product or platform. Deep learning has already proved useful in many software disciplines, including computer vision, speech and audio processing, natural language processing, robotics, bioinformatics and chemistry, video games, search engines,
The course is organized in three parts. The first one introduces the basic machine learning concepts. The second one describes the most established deep learning algorithms, which are essentially solved technologies. The final part describes more speculative ideas that are widely believed to be important for future research in deep learning. Contents: 1. Machine learning basics 2. Deep feedforward networks 3. Regularization 4. Convolutional networks 5. Sequence modeling a. Deep recurrent neural networks b. Recursive neural networks c. Echo state networks d. Long short term memory 6. Applications 7. Linear factor models a. Probabilistic PCA b. Independent component analysis c. Sparse coding 8. Autoencoders 9. Representation learning 10. Structered probabilistic models 11. Montecarlo methods 12. Approximate inference 13. Deep generative models
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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