add few tutorials (under construction)

tutorials/machine_learning/README.md

@@ -0,0 +1,25 @@
+## Tutorials about machine learning
+
+This folder contains tutorials about different learning models and algorithms used.
+
+## References
+
+Prerequisites: some tutorials require the student to be familiar with multivariate calculus, linear algebra, probability and statistics, information theory, and other mathematical fields.
+
+Here are references/tutorials that I have watched/read, with their corresponding level: 
+1. "Machine Learning", by Prof. Andrew Ng. (Easy + practical)
+2. "CS188: Introduction to Artificial Intelligence", (Easy + theoretical/practical)
+3. "Learning from Data" Yaser (Medium + theoritical)
+4. "Machine Learning", by Prof. Nado de Freitas, 2013 (Medium + theoretical)
+5. "Deep Learning" (www.deeplearningbook.org), Goodfellow et al., 2016 (Easy)
+6. "Gaussian Processes for Machine Learning", Rasmussen and Williams, 2006 (Medium)
+7. "Pattern Recognition and Machine Learning", Bishop, 2006 (Hard)
+8. "Deep Learning for Computer Vision" (Easy)
+9. "Deep Learning for NLP" (Medium)
+10. "Reinforcement Learning" (http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html), Silver, UC London, 2015 (Medium)
+11. "An Introduction to Reinforcement Learning", Sutton and Barto, 2018 (Medium)
+12. "Deep Reinforcement Learning", (Medium/Hard)
+
+Are there other stuffs to learn?
+Yes, probabilistic graphical models, variational inference, information geometry, etc.
+