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School of engineering in Physics, Applied Physics, Electronics & Materials
Science
> Studies
This course is intended to provide the necessary foundation for understanding, analyzing and designing neuromimetic networks. It covers the field of statistical learning in pattern recognition, as well as the basics of quantitative assessment of learning. An introduction will be given on deep learning as an extension of multi-layered perceptrons. This course is aimed at students from a variety of disciplines and is designed to be accessible to understanding by students who are not familiar with mathematical techniques.
Contact Marion DOHEN, Anne GUERIN DUGUEIntroduction
Mathematical models: from neuron to networks
Linear models, Associative memories
Nonlinear models, Multilayer Perceptron
Introduction to Deep Learning
Self-organizing networks
Sources separation
Basic knowledge of mathematics
Written Exam (Exam1, Exam2)
CC: Computer exercice
SESSION 1 (présentiel) : 30% CC + 70% exam
SESSION 2 (présentiel) : 30% CC (session 1) + 70% exam
SESSION 1 (à distance) : 30% CC + 70% exam
SESSION 2 (à distance) : 30% CC (session 1) + 70% exam
Apprentissage statistique, G. Dreyfus, J.M. Martinez, M. Samuelides, M.B. Gordon, F. Badran, S. Thiria, Eyrolles, 2008
Date of update April 4, 2018
3 Parvis Louis Néel - CS 50257 - 38016 Grenoble Cedex 1
