Books
From Research management course
Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees
Education in Machine learning has changed drastically in recent years. The main roadstones were deep learning, reinforcement learning, and now it is physics-informed or geometric deep learning. The requirements for mathematical knowledge rise, even for engineering parts. An example is differential programming techniques. Below we present bachelor and master programs for modern Machine learning. We call it Knowledge-aware machine learning.
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