Consiglio Nazionale delle Ricerche

Tipo di prodottoMateriale didattico
TitoloDL01 - Machine Learning Basics
Anno di pubblicazione2018
Autore/iCristina De Castro, IEIIT-CNR
Affiliazioni autoriIEIIT-CNR
Autori CNR e affiliazioni
  • inglese
SintesiThis lecture is an introduction to the basics of Machine Learning and belongs to a wider series including neural networks, convolutional neural networks, Bayesian probability and Big Learning with Bayesian Methods. First, what learning means is explained. Then the concepts of supervised and unsupervised learning. Afterwards, the difference between optimization and machine learning. It follows an appearent stop: the no free lunch theorem, but distributions and parameter estimation will help to recover. In particular, Maximum Likelihood Estimation. Then another concept, Stochastic Gradient Descent. Finally, what lacks in machine learning that motivates Deep Learning.
Lingua sintesieng
Altra sintesi-
Lingua altra sintesi-
Parole chiavemachine learning, supervised, unsupervised, no free lunch theorem, maximum likelihood estimation, stochastic gradient descent, deep learning
Link (URL, URI)-
Note/Altre informazioni-
Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
Moduli CNR-
Progetti Europei-
  • DL01 - Machine Learning Basics