LaTeX – a high-quality typesetting system for all computing platforms. It is free.

TikZ and PGF are TeX packages for creating sophisticated graphics programmatically. TikZ is built on top of PGF.

Overleaf is a collaborative, cloud-based LaTeX editor used for writing, editing and publishing scientific documents. Features include real-time collaboration, version control, and hundreds of ready to use LaTeX templates.

MathPix extracts equations as LaTeX code from PDFs or handwritten notes.

Grammarly – an AI-powered writing assistant.

The Julia Language: A fresh approach to technical computing.

H. Pishro-Nik Introduction to Probability, Statistics, and Random Processes (an excellent, free, online book in HTML format).

A visual introduction to probability and statistics.

3Blue1Brown channel by Grant Sanderson. Explanations are driven by animations. Difficult problems are made simple with changes in perspective.

A visual introduction to machine learning.

Making hard ideas intuitive.

An Interactive linear algebra book

A visual introduction to machine learning

Visualizing algorithms.

R. L. Graham, D. E. Knuth, and O. Patashnik, Concrete Mathematics: A Foundation for Computer Science, Second ed. Boston, MA: Addison-Wesley Professional, 1994, isbn: 978-0201558029.

M. J. Kochenderfer and T. A. Wheeler, Algorithms for Optimization. Cambridge, MA: The MIT Press, 2019, isbn: 978-0262039420.

I. Stewart, In Pursuit of the Unknown: 17 Equations That Changed the World. New York, NY: Basic Books, 2013, isbn: 978-0465085989.

D. P. Bertsekas and J. N. Tsitsiklis, Introduction to Probability, Second ed. Cambridge, Massachusetts: Athena Scientific, 2008, isbn: 978-1886529236.

J. K. Blitzstein and J. Hwang, Introduction to Probability, Second ed. Boca Raton, Florida: Chapman & Hall/CRC, 2019, isbn: 978-1138369917.

F. Dekking, C. Kraaikamp, H. Lopuhaa, and L. Meester, A Modern Introduction to Probability and Statistics: Understanding Why and How, ser. Springer Texts in Statistics. Berlin, Germany: Springer, 2007, isbn: 978-1852338961.

S. Ghahramani, Fundamentals of Probability: With Stochastic Processes, Fourth ed. Boca Raton, Florida: Chapman & Hall/CRC, 2018, isbn: 978-1498755092.

M. Mitzenmacher and E. Upfal, Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis, Second ed. Cambridge, UK: Cambridge University Press, 2017, isbn: 978-1107154889.

D. J. Morin, Probability: For the Enthusiastic Beginner. CreateSpace, 2016, isbn: 978-1523318674.

H. Pishro-Nik, Introduction to Probability, Statistics, and Random Processes. Kappa Research, 2014, isbn: 978-0990637202.

C. Davidson-Pilon, Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference, ser. Addison-Wesley Data & Analytics. Boston, MA: Addison-Wesley Professional, 2015, isbn: 978-0133902839.

J. Kruschke, Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second ed. Cambridge, Massachusetts: Academic Press, 2014, isbn: 978-0124058880.

R. Dechter, Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second ed., ser. Synthesis Lectures on Artificial Intelligence and Machine Learning. San Rafael, California: Morgan & Claypool Publishers, 2019, isbn: 978-1681734903.

F. Jensen and T. D. Nielsen, Bayesian Networks and Decision Graphs, ser. Information Science and Statistics. Berlin, Germany: Springer, 2007, isbn: 978-0387682815.

M. I. Jordan, Ed., Learning in Graphical Models, ser. Adaptive Computation and Machine Learning. Cambridge, Massachusetts: The MIT Press, 1998, isbn: 978-0262029445.

U. B. Kjarulff and A. L. Madsen, Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second ed., ser. Information Science and Statistics. Berlin, Germany: Springer, 2015, isbn: 978-1461451037.

D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques. Cambridge, Massachusetts: The MIT Press, 2009, isbn: 978-0262013192.

R. E. Neapolitan, Learning Bayesian Networks. London, UK: Pearson, 2019, isbn: 978-0130125347.

J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, ser. Morgan Kaufmann Series in Representation and Reasoning. Burlington, MA: Morgan Kaufmann, 1988, isbn: 978-1558604797.

L. E. Sucar, Probabilistic Graphical Models: Principles and Applications, ser. Advances in Computer Vision and Pattern Recognition. Springer, 2015, isbn: 978-1447166986.

A. Darwiche, Modeling and Reasoning with Bayesian Networks. Cambridge, UK: Cambridge University Press, 2014, isbn: 978-1107678422.

N. Fenton and M. Neil, Risk Assessment and Decision Analysis with Bayesian Networks, Second ed. Boca Raton, Florida: Chapman and Hall/CRC, 2018, isbn: 978-1138035119.

Q. Ji, Probabilistic Graphical Models for Computer Vision. Cambridge, Massachusetts: Academic Press, 2019, isbn: 978-0128034675.

M. J. Kochenderfer, Decision Making Under Uncertainty: Theory and Application. Cambridge, Massachusetts: The MIT Press, 2015, isbn: 978-0262029254.

D. J. C. MacKay, Information Theory, Inference and Learning Algorithms. Cambridge, UK: Cambridge University Press, 2003, isbn: 978-0521642989.

A. Pfeffer, Practical Probabilistic Programming. Shelter Island, NY: Manning Publications, 2016, isbn: 978-1617292330.

S. Watanabe and J.-T. Chien, Bayesian Speech and Language Processing. Cambridge, UK: Cambridge University Press, 2015, isbn: 978-1107055575.

A visual introduction to probability and statistics.

PGM Learning – an interactive, educational web app.

libDAI – a free and open source C++ library for discrete approximate inference in graphical models.

OpenGM – a C++ template library for discrete factor graph models and distributive operations on these models.

Sensitivity Analysis, Modeling, Inference and More (SAMIAM) – a comprehensive tool for modeling and reasoning with Bayesian networks.

Bayes Net toolbox for Matlab.

pgmpy – a Python library for probabilistic graphical models.

PyMC3 – probabilistic programming in Python.

Concise course notes – Stanford’s CS228: Probabilistic Graphical Models.

Kevin Patrick Murphy – Machine Learning: a Probabilistic Perspective.

David Barber – Bayesian Reasoning and Machine Learning book.

Chris Bishop – Pattern Recognition and Machine Learning.

David J. C. Mackay – Information Theory, Inference, and Learning Algorithms book.

Martin J. Wainwright and Michael I. Jordan – Graphical models, exponential families, and variational inference book.