Difference between revisions of "Books"
From Research management course
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=Bayesian statistics and inference= | =Bayesian statistics and inference= | ||
| + | # [http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf Bayesian reasoning and machine learning by David Barber, 2014] | ||
| + | # Probabilistic graphical models by Daphne Koller and Nir Friedman, 2009 | ||
| + | # [https://doc.lagout.org/science/Artificial%20Intelligence/Machine%20learning/Machine%20Learning_%20A%20Probabilistic%20Perspective%20%5BMurphy%202012-08-24%5D.pdf Machine learning: a probabilistic perspective by Kevin P. Murphy, 2012] | ||
=Functional data analysis= | =Functional data analysis= | ||
Revision as of 00:46, 30 December 2020
Contents
Machine learning for beginners
- A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2017-2018
- Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David, 2014
- Mathematics for Machine learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, 2020
- Mathematics for Physicists: Introductory Concepts and Methods by Alexander Altland & Jan von Delf, 2018
- Bishop C.P. Pattern recognition and machine learning, Berlin: Springer, 2008.
- MackKay D. Information Theory, Pattern Recognition and Neural Networks, Inference.org.uk, 2009.
Linear algebra
- Linear algebra by Jörg Liesen, Volker Mehrmann, 2015
- Linear algebra by Jim Hefferon, 2017
- Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe, 2018
Basic statistics
Bayesian statistics and inference
- Bayesian reasoning and machine learning by David Barber, 2014
- Probabilistic graphical models by Daphne Koller and Nir Friedman, 2009
- Machine learning: a probabilistic perspective by Kevin P. Murphy, 2012