Ultimo aggiornamento: 05-09-2016

Per la rete ho trovato un serie di link utili, li scrivo come nota personale e anche perchè  del materiale  potrebbe servire a molti.

• Corsi della Stanford University sull’IA:
  • CS94SI: What is AI?
  • CS231n: Convolutional Neural Networks for Visual Recognition: SVM, Gradient, Brackpropagation, Neural Networks.
  • CS221: Artificial Intelligence: Principles and Techniques: Bayesian inference, Hidden Markov models, Expectation Maximization (EM).
  • CS229 Machine Learning
  • CS369L: Algorithmic Perspective on Machine Learning: Nonnegative matrix factorization (SVD), Tensor Decomposition, ecc..
• Corsi del MIT:
  • Artificial Intelligence 2010, ci sono le video-lezioni su: Neural Nets, Deep Learning, Genetic Algo, ecc…
  • Machine Learning 2006 : kernel, SVM, EM, HMM,ecc…
• Corsi della University of Washington State:
  • CSE 415: Introduction To Artificial Intelligence (for non-majors)
  • CSE 446: Machine Learning
  • CSE 455: Computer Vision
  • CSE 473: Introduction To Artificial Intelligence
• Il blog di Sebastian Ruder
  • An overview of gradient descent optimization algorithms
• Hello, TensorFlow! Building and training your first TensorFlow graph from the ground up

Video Lezioni:

• Andrew Ng – Machine Learning at Stanford University
• Andrew Ng – Videolezioni su Coursera (più facili)

Altro materiale utile:

• Stanford University CS109: Intro to Probability for Computer Scientists: ci sono anche i link ai video su youtube
• MIT Randomized Algorithms
• A Beginner’s Guide To Understanding Convolutional Neural Networks