Difference between revisions of "Books"
From Research management course
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* [https://mml-book.github.io/book/mml-book.pdf Mathematics for Machine learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, 2020] | * [https://mml-book.github.io/book/mml-book.pdf Mathematics for Machine learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, 2020] | ||
* [https://klassfeldtheorie.files.wordpress.com/2018/10/mathematische-methoden-310117.pdf Mathematics for Physicists: Introductory Concepts and Methods by Alexander Altland & Jan von Delf, 2018] | * [https://klassfeldtheorie.files.wordpress.com/2018/10/mathematische-methoden-310117.pdf Mathematics for Physicists: Introductory Concepts and Methods by Alexander Altland & Jan von Delf, 2018] | ||
| − | * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop C.P. Pattern recognition and machine learning, Berlin: Springer, 2008 | + | * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop C.P. Pattern recognition and machine learning, Berlin: Springer, 2008] |
| − | * [http://www.inference.org.uk/itprnn/book.pdf MackKay D. Information Theory, Pattern Recognition and Neural Networks, Inference.org.uk, 2009 | + | * [http://www.inference.org.uk/itprnn/book.pdf MackKay D. Information Theory, Pattern Recognition and Neural Networks, Inference.org.uk, 2009] |
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie Robert Tibshirani Jerome Friedman, 2008] | * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie Robert Tibshirani Jerome Friedman, 2008] | ||
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=Functional data analysis= | =Functional data analysis= | ||
* Functional data analysis by J.O. Ramsay and B.W. Silverman, 2005 | * Functional data analysis by J.O. Ramsay and B.W. Silverman, 2005 | ||
| − | * [https://www.unige.ch/~hairer/poly-sde-mani.pdf Solving Differential Equations on Manifolds by Ernst Hairer, 2011 | + | * [https://www.unige.ch/~hairer/poly-sde-mani.pdf Solving Differential Equations on Manifolds by Ernst Hairer, 2011] |
=Programming= | =Programming= | ||
| Line 35: | Line 35: | ||
=Russian edition= | =Russian edition= | ||
| − | * [http://www.1variant.ru/content/uchebniki/matematika/650.pdf Лагутин М.Б. Наглядная математическая статистика, 2009] | + | * [http://www.1variant.ru/content/uchebniki/matematika/650.pdf Лагутин М.Б. Наглядная математическая статистика, 2009] (cм. также [http://files.lbz.ru/pdf/cC2125-4-ch.pdf вырезку]) |
* [https://www.artlebedev.ru/izdal/spravochnik-izdatelya-i-avtora/ Аркадий Мильчин и Людмила Чельцова. Справочник издателя и автора (Редакционно-издательское оформление издания), 2018] | * [https://www.artlebedev.ru/izdal/spravochnik-izdatelya-i-avtora/ Аркадий Мильчин и Людмила Чельцова. Справочник издателя и автора (Редакционно-издательское оформление издания), 2018] | ||
Revision as of 01:51, 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
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie Robert Tibshirani Jerome Friedman, 2008
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
Optimization
- Convex Optimization by Stephen Boyd and Lieven Vandenberghe, 2009
- Iterative Methods for Optimization by C.T.Kelley, 1999
Basic statistics
- Probability Theory Alexandr A. Borovkov, 2006
- Mathematical statistics by A.A. Borovkov, 1999
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
Functional data analysis
- Functional data analysis by J.O. Ramsay and B.W. Silverman, 2005
- Solving Differential Equations on Manifolds by Ernst Hairer, 2011
Programming
- Python notes for professionals by GoalKicker.com Free Programming Books, 2020
- Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, 2020