Difference between revisions of "Books"
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* [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] | ||
=Linear algebra= | =Linear algebra= | ||
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*[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= | ||
| + | * [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] | ||
=Basic statistics= | =Basic statistics= | ||
| + | |||
=Bayesian statistics and inference= | =Bayesian statistics and inference= | ||
Revision as of 01:23, 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
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
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