Difference between revisions of "Books"
From Research management course
m (→Programming) |
|||
| Line 1: | Line 1: | ||
| − | =Machine learning for beginners= | + | ==Machine learning for beginners== |
* [https://arxiv.org/pdf/1709.02840 A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2017-2018] | * [https://arxiv.org/pdf/1709.02840 A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2017-2018] | ||
* [https://www.semanticscholar.org/paper/Understanding-Machine-Learning%3A-From-Theory-to-Shalev-Shwartz-Ben-David/ce615ae61d67db8537e981a0a08da7f0f2ff1cee Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David, 2014] | * [https://www.semanticscholar.org/paper/Understanding-Machine-Learning%3A-From-Theory-to-Shalev-Shwartz-Ben-David/ce615ae61d67db8537e981a0a08da7f0f2ff1cee Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David, 2014] | ||
| Line 8: | Line 8: | ||
* [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] | ||
| − | =Linear algebra= | + | ==Linear algebra== |
*[https://drive.google.com/file/d/16SL9bQYar2ylDzHKNapBIDXCYUZEnRIh/view?fbclid=IwAR3fiuQgDJ0PRxv8o6UslbGx2ICdKxO2Li32FtwPJ_GbjRCXKhxa-BPZw2A Linear algebra by Jörg Liesen, Volker Mehrmann, 2015] | *[https://drive.google.com/file/d/16SL9bQYar2ylDzHKNapBIDXCYUZEnRIh/view?fbclid=IwAR3fiuQgDJ0PRxv8o6UslbGx2ICdKxO2Li32FtwPJ_GbjRCXKhxa-BPZw2A Linear algebra by Jörg Liesen, Volker Mehrmann, 2015] | ||
*[http://joshua.smcvt.edu/linearalgebra Linear algebra by Jim Hefferon, 2017] | *[http://joshua.smcvt.edu/linearalgebra Linear algebra by Jim Hefferon, 2017] | ||
*[https://web.stanford.edu/~boyd/vmls/?fbclid=IwAR08VCHfJ1hVAvuVBW6G59CZZ9EWzAlm0yKnID82DP9G2YbmugzsYIQQ4W0 Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe, 2018] | *[https://web.stanford.edu/~boyd/vmls/?fbclid=IwAR08VCHfJ1hVAvuVBW6G59CZZ9EWzAlm0yKnID82DP9G2YbmugzsYIQQ4W0 Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe, 2018] | ||
| − | =Optimization= | + | ==Optimization== |
* [https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf Convex Optimization by Stephen Boyd and Lieven Vandenberghe, 2009] | * [https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf Convex Optimization by Stephen Boyd and Lieven Vandenberghe, 2009] | ||
* [https://archive.siam.org/books/textbooks/fr18_book.pdf Iterative Methods for Optimization by C.T.Kelley, 1999] | * [https://archive.siam.org/books/textbooks/fr18_book.pdf Iterative Methods for Optimization by C.T.Kelley, 1999] | ||
| − | =Basics of probability and statistics= | + | ==Basics of probability and statistics== |
* A first course in probability by Sheldon M. Ross, 2012 | * A first course in probability by Sheldon M. Ross, 2012 | ||
* Elements of information theory by Thomas M. Cover, Joy A. Thomas, 2006 | * Elements of information theory by Thomas M. Cover, Joy A. Thomas, 2006 | ||
| Line 25: | Line 25: | ||
* Linear Models and Generalizations: Least Squares and Alternatives by C. Radhakrishna Rao et al., 2007 | * Linear Models and Generalizations: Least Squares and Alternatives by C. Radhakrishna Rao et al., 2007 | ||
| − | =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] | * [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 | * Probabilistic graphical models by Daphne Koller and Nir Friedman, 2009 | ||
| Line 31: | Line 31: | ||
* Bayesian data analysis by Andrew Gelman et al., 2013 | * Bayesian data analysis by Andrew Gelman et al., 2013 | ||
| − | =Functional data analysis= | + | ==Functional data analysis== |
* Sequences and series in banach spaces by J. Diestel, 1984 | * Sequences and series in banach spaces by J. Diestel, 1984 | ||
* Functional data analysis by J.O. Ramsay and B.W. Silverman, 2005 | * Functional data analysis by J.O. Ramsay and B.W. Silverman, 2005 | ||
| Line 46: | Line 46: | ||
* [https://d2l.ai/ Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, 2020] | * [https://d2l.ai/ Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, 2020] | ||
| − | =Russian edition= | + | ==Russian edition== |
* [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]) | * [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 23:40, 5 April 2021
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
Basics of probability and statistics
- A first course in probability by Sheldon M. Ross, 2012
- Elements of information theory by Thomas M. Cover, Joy A. Thomas, 2006
- Probability theory by Alexandr A. Borovkov, 2006
- Mathematical statistics by Alexandr A. Borovkov, 1999
- Linear Statistical Inference & Its Applications by C. Radhakrishna Rao, 1967
- Linear Models and Generalizations: Least Squares and Alternatives by C. Radhakrishna Rao et al., 2007
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
- Bayesian data analysis by Andrew Gelman et al., 2013
Functional data analysis
- Sequences and series in banach spaces by J. Diestel, 1984
- Functional data analysis by J.O. Ramsay and B.W. Silverman, 2005
- Solving Differential Equations on Manifolds by Ernst Hairer, 2011
Discrete analysis
- Lectures on discrete geometry by Jiří Matoušek, 2002
- Indiscrete thoughts by Gian-Carlo Rota, 2008
- Graph theory by Reinhard Diestel, 2017
- Graph theory (groups and symmetries: from finite groups to Lie groups) by Reinhard Diestel, 2000
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