Miscellaneous

Open Access Books

General Machine Learning:

• Probabilistic Machine Learning: An Introduction 1ed (2022) by Kevin P. Murphy.
• Probabilistic Machine Learning: Advanced Topics 1ed (2023) by Kevin P. Murphy.
• Pattern Recognition and Machine Learning 1ed (2006) by Christopher Bishop.
• Information Theory, Inference, and Learning Algorithms 1ed (2003) by David MacKay.
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2ed (2009) by Trevor Hastie, Robert Tibshirani, Jerome Friedman.
• Bayesian Reasoning and Machine Learning 1ed (2012) by David Barber.

Deep Learning:

• Deep Learning 1ed (2016) by Ian Goodfellow, Yoshua Bengio and Aaron Courville.

Reinforcement Learning:

• Reinforcement Learning: An Introduction 2ed (2017) by Richard S. Sutton and Andrew G. Barto.

Gaussian Processes:

• Gaussian Processes for Machine Learning 1ed (2006) by Carl Edward Rasmussen and Christopher K. I. Williams.

Statistics:

• Computer Age Statistical Inference: Algorithms, Evidence and Data Science 1ed (2016) by Bradley Efron and Trevor Hastie.