pyrobolearn/tutorials/machine_learning

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.