1. Pattern Recognition and Machine Learning - Christopher M. Bishop
2. Machine Learning: A Probabilistic Perspective - Kevin P. Murphy
3. Probabilistic Machine Learning: An Introduction - Kevin P. Murphy
4. Probabilistic Machine Learning: Advanced Topics - Kevin P. Murphy
5. Deep Learning - Ian Goodfellow, Yoshua Bengio, and Aaron Courville
6. The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, and Jerome Friedman
7. Bayesian Reasoning and Machine Learning - David Barber
8. Probabilistic Graphical Models: Principles and Techniques - Daphne Koller and Nir Friedman
9. Information Theory, Inference, and Learning Algorithms - David J.C. MacKay
10. Pattern Classification - Richard O. Duda, Peter E. Hart, and David G. Stork
11. Understanding Machine Learning: From Theory to Algorithms - Shai Shalev-Shwartz and Shai Ben-David
12. Machine Learning Yearning - Andrew Ng
13. Artificial Intelligence: A Modern Approach - Stuart Russell and Peter Norvig
14. Machine Learning: An Algorithmic Perspective - Stephen Marsland
15. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Aurélien Géron
16. Deep Learning - Christopher M. Bishop
17. Applied Predictive Modeling - Max Kuhn and Kjell Johnson
18. Hamiltonian Monte Carlo Methods in Machine Learning - Tshilidzi Marwala, Rendani Mbuvha, Wilson Tsakane Mongwe
19. Probabilistic Machine Learning for Civil Engineers - James Goulet
20. Inference and Learning from Data (Vol. 1-3) - Ali H. Sayed
21. Kalman Filter from the Ground Up - Alex Becker
22. The 100-Page Machine Learning Handbook - Andriy Burkov
23. Gaussian Processes for Machine Learning - Carl Edward Rasmussen, Christopher K. I. Williams
24. Machine Learning Engineering - Andriy Burkov
25. Neural Networks for Pattern Recognition - Christopher M. Bishop
26. Understanding Deep Learning - Simon J.D. Prince
27. Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series) - Richard S. Sutton
28. Deep Learning for Computer Vision with Python - Adrian Rosenbrock
29. Mathematics for Machine Learning - Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
30. Graph Neural Networks: Foundations, Frontiers, and Applications - Jian Pei, Liang Zhao, Lingfei Wu, Peng Cui
33. All the Mathematics You Missed: But Need to Know for Graduate School - Thomas A. Garrity, Lori Pedersen
34. Fourier Series - Georgi P. Tolstov
35. Deep Learning from the Basics to Practice - Andrew Glassner
36. Hands-On Mathematics for Deep Learning - Jay Dawani
37. All of Statistics - Larry Wasserman
38. Convex Optimization - Stephen Boyd, Lieven Vandenberghe
39. Handbook of Machine Learning (Vol 1-2) - Tshilidzi Marwala, Collins Leke
40. Computational Intelligence for Missing Data Imputation, Estimation and Management - Tshilidzi Marwala
41. Python Tricks: A Buffet of Awesome Python Features - Dan Bader
42. Professional C++ - Marc Gregoire
43. Bayesian Data Analysis - Andrew Gelman
44. High Performance Computing - John Levesque
45. Dive into Deep Learning - Aston Zhang
46. Bayesian Models of Cognition: Reverse Engineering the Mind - Thomas L. Griffiths, Nick Chater and Joshua Tenenbaum
47. Deep Learning Architectures-A Mathematical Approach - Ovidiu Calin
48. Geometric Deep Learning:Grids, Groups, Graphs, Geodesics, and Gauges - Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković
49. Geometry of Deep Learning-A Signal Processing Perspective - Jong Chul Ye
50. High-Dimensional Probability: An Introduction with Applications in Data Science- Roman Vershynin
51. Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning-Jean Gallier
52. Data driven science and engineering - Steven Brunton
53. Deep learning on graphs-Yao Ma
54. Mathematical theory of deep learning- Philipp Petersen
55. [Mathematical Foundations of Geometric Deep Learning] (https://arxiv.org/pdf/2508.02723)- Haitz Saez de Oc ´ ariz Borde and Michael Bronstein
57. Aalto Dictionary of Machine Learning (ADictML)-Alex Jung
58. The Principles of Deep Learning Theory-Daniel A. Roberts and Sho Yaida
59. Machine Learning-A Bayesian and Optimization Perspective-Sergios Theodoridis
60. Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory - Arnulf Jentzen
Please note that I am not a big fan of video tutorials, and I might have omitted some sites you love. Feel free to add those as you see fit.
- Brilliant.org
- The Bright Side of Mathematics
- 3Blue1Brown
- Tibees
- Veritasium
- BayesWorks
- Andrej Karpathy
- StatQuest
- Machine Learning Street Talk
- Yannic Kilcher
- MathWorld by Wolfram
- Deep Learning Bible
- Steve Brunton
- Machine Learning Mastery
- DeepLearning.AI
- Lex Fridman
- ritvikmath
- BASIRA Lab
I find these handy for research.