Machine Learning II course : References

• Information theory, inference, and learning algorithms : David MacKay (free)
• Elements of information theory (2nd edition) : Thomas Cover & Joy Thomas
• Notes by Jérémy Bensadon of courses by Yann Ollivier

• Reinforcement Learning: An Introduction : Richard S. Sutton & Andrew G. Barto : 2017 version, 1998 version (this one referred to in the course)
• Statistical Learning and Sequential Prediction : Alexander Rakhlin & Karthik Sridharan
• Reinforcement Learning course by Rémi Munos

• Pattern recognition and machine learning : Christopher Bishop
• The elements of statistical learning (data mining, inference, and prediction) : Trevor Hastie, Robert Tibshirani & Jerome Friedman
• Gaussian processes for Machine Learning : Carl Rasmussen and Christopher Williams