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Single discipline educational activity
Course Sheet Academic Year of enrolment:
Disciplinary Sector:
Probability and Mathematical Statistics
Professor and Collaborators:
CAROLI COSTANTINI Cristina
Hours of classroom activity:
Prerequisites:
The student must have the basic notions of calculus and linear algebra.
Objectives
Contents Feedforward deep neural networks
Extended Syllabus 1. Elements of probability and information theory
2.Fundamentals of machine learning
3.Feedforward deep neural networks
4. Applications
Recommended Bibliography Goodfellow, I.; Bengio, Y.; Courville, A.: Deep learning, MIT Press (2016)
Methods of Provision
Teaching Methods
Evaluation methods Verification of learning:
Knowledge and understanding
Knowledge and understanding will be verified by means of a written test and a an oral exam.
The written test will include exercises and problems on all the topics of the course.
The oral exam will test knowledge of the theory (definitions, statements, proofs, examples).
The final grade will be expressed on a scale from 1 to 30, and will take into account also the group project described below.
Ability to apply knowledge and understanding
The ability to apply knowledge and understanding will be verified both by the written test described above and by a group project that will develop some application defined by the teacher.
At the end of the project students will write a report that will discuss the methods they have chosen and the outcomes of the project. The report will be submitted to the teacher at least one week before the oral exam.