Difference between revisions of "Books"
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==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] | ||
Revision as of 22:39, 20 February 2023
Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees
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
- Functional Analysis by Peter D. Lax, 2002.
- 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