| 1. Learning Theory from First Principles | 2. Distributional Reinforcement Learning | 3. Probabilistic Machine Learning: An Introduction |
|---|---|---|
| By: Francis Bach Year: 2023 Publisher: MIT Press |
By: Marc G. Bellemare and Will Dabney, and Mark Rowland Year: 2023 Publisher: MIT Press |
By: Kevin P. Murphy Year: 2023 Publisher: MIT Press |
| Front Cover Thumbnail Not Found! | Front Cover Thumbnail Not Found! |  |
| 4. Understanding Deep Learning | 5. High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications | 6. Speech and Language Processing |
|---|---|---|
| By: Simon J.D. Prince Year: 2023 Publisher: MIT Press |
By: John Wright and Yi Ma Year: 2022 Publisher: Cambridge University Press |
By: Dan Jurafsky and James H. Martin Year: 2022 Edition: 3 |
| Front Cover Thumbnail Not Found! |  |
Front Cover Thumbnail Not Found! |
| 7. Probabilistic Machine Learning: An Introduction | 8. Model-based Machine Learning | 9. Probabilistic Numerics |
|---|---|---|
| By: Kevin P. Murphy Year: 2022 Publisher: MIT Press |
By: John Winn, Christopher M. Bishop, Thomas Diethe, John Guiver and Yordan Zaykov Year: 2022 |
By: Philipp Hennig, Michael A. Osborne, Hans Kersting Year: 2022 Publisher: Cambridge University Press |
 |
Front Cover Thumbnail Not Found! |  |
| 10. Hands-On Data Visualization | 11. The Principles of Deep Learning Theory | 12. Algorithms for Decision Making |
|---|---|---|
| By: Jack Dougherty and Ilya Ilyankou Year: 2022 Publisher: O'REILLY |
By: Daniel A. Roberts, Sho Yaida, and Boris Hanin Year: 2022 Publisher: Cambridge University Press |
By: Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray Year: 2022 Publisher: MIT Press |
 |
 |
 |
| 13. Large-Scale Convex Optimization | 14. Machine Learning - A First Course for Engineers and Scientists | 15. Introduction to Online Convex Optimization |
|---|---|---|
| By: Ernest K. Ryu, and Wotao Yin Year: 2022 Publisher: Cambridge University Press |
By: Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön Year: 2022 Publisher: Cambridge University Press |
By: Elad Hazan Year: 2022 Publisher: MIT Press Edition: 2 |
 |
 |
 |
| 16. Reinforcement Learning: Theory and Algorithms | 17. Dive into Deep Learning | 18. An Introduction to Statistical Learning |
|---|---|---|
| By: Alekh Agarwal, Nan Jiang, Sham M. Kakade, and Wen Sun Year: 2022 |
By: Aston Zhang, Zachary C. Lipton, Mu Li and Alexander J. Smola Year: 2021 |
By: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani Year: 2021 Publisher: Springer Edition: 2 |
| Front Cover Thumbnail Not Found! |  |
 |
| 19. Computer Vision: Algorithms and Applications | 20. Think Bayes | 21. Machine Learning and Big Data |
|---|---|---|
| By: Richard Szeliski Year: 2021 Publisher: Springer Edition: 2 |
By: Allen B. Downey Year: 2021 Publisher: O'REILLY Edition: 2 |
By: Kareem Alkaseer Year: 2021 |
 |
 |
Front Cover Thumbnail Not Found! |
| 22. Theory of Computation | 23. Introduction to Probability for Data Science | 24. Physics-based Deep Learning Book |
|---|---|---|
| By: Jim Hefferon Year: 2021 |
By: Stanley H. Chan Year: 2021 Publisher: Michigan Publishing |
By: Nils Thuerey, Philipp Holl, Maximilian Mueller, Patrick Schnell, and Felix Trost and Kiwon Um Year: 2021 Edition: 0.2 |
 |
 |
 |
| 25. Bayes Rules! | 26. Deep Learning on Graphs | 27. Data Science at the Command Line |
|---|---|---|
| By: Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu Year: 2021 Publisher: CRC Press |
By: Yao Ma, and Jiliang Tang Year: 2021 Publisher: Cambridge University Press |
By: Jeroen Janssens Year: 2021 Publisher: O'REILLY Edition: 2 |
 |
 |
 |
| 28. Deep Learning Interviews | 29. Learning Statistical Models Through Simulation in R | 30. Linear Algebra |
|---|---|---|
| By: Shlomo Kashani, and Amir Ivry Year: 2021 Publisher: Interviews AI |
By: Dale J Barr Year: 2021 |
By: Jim Hefferon Year: 2020 Publisher: leanpub Edition: 4 |
| Front Cover Thumbnail Not Found! | Front Cover Thumbnail Not Found! |  |
| 31. Bayesian Reasoning and Machine Learning | 32. Mathematics for Machine Learning | 33. Automated Machine Learning |
|---|---|---|
| By: David Barber Year: 2020 Publisher: Cambridge University Press |
By: Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Year: 2020 Publisher: Cambridge University Press |
By: Frank Hutter, Lars Kotthoff and Joaquin Vanschoren Year: 2019 Publisher: Springer |
 |
 |
 |
| 34. Introduction to Probability | 35. Fairness and Machine Learning | 36. Machine Learning Yearning |
|---|---|---|
| By: Joseph K. Blitzstein and Jessica Hwang Year: 2019 Publisher: Routledge Edition: 2 |
By: Solon Barocas, Moritz Hardt, and Arvind Narayanan Year: 2019 |
By: Andrew Ng Year: 2018 |
 |
Front Cover Thumbnail Not Found! | Front Cover Thumbnail Not Found! |
| 37. Reinforcement Learning: An Introduction | 38. Foundations of Machine Learning | 39. Adversarial Robustness - Theory and Practice |
|---|---|---|
| By: Richard S. Sutton and Andrew G. Barto Year: 2018 Publisher: MIT Press Edition: 2 |
By: Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar Year: 2018 Publisher: MIT Press Edition: 2 |
By: Zico Kolter and Aleksander Madry Year: 2018 |
 |
 |
Front Cover Thumbnail Not Found! |
| 40. A Course in Machine Learning | 41. Deep Learning | 42. Python Data Science Handbook |
|---|---|---|
| By: Hal Daumé III Year: 2017 |
By: Ian Goodfellow, Yoshua Bengio and Aaron Courville Year: 2016 Publisher: MIT Press |
By: Jake VanderPlas Year: 2016 Publisher: O'REILLY |
 |
 |
 |
| 43. Computer Age Statistical Inference: Algorithms, Evidence and Data Science | 44. Statistical Learning with Sparsity: The Lasso and Generalizations | 45. Statistical inference for data science |
|---|---|---|
| By: Bradley Efron, and Trevor Hastie Year: 2016 Publisher: Cambridge University Press |
By: Trevor Hastie, Robert Tibshirani and Martin Wainwright Year: 2015 Publisher: Routledge |
By: Brian Caffo Year: 2015 Publisher: leanpub |
 |
 |
 |
| 46. Neural Networks and Deep Learning | 47. Think Stats | 48. Understanding Machine Learning: From Theory to Algorithms |
|---|---|---|
| By: Michael Nielsen Year: 2015 Publisher: Determination Press |
By: Allen B. Downey Year: 2014 Publisher: O'REILLY Edition: 2 |
By: Shai Shalev-Shwartz and Shai Ben-David Year: 2014 Publisher: Cambridge University Press |
| Front Cover Thumbnail Not Found! |  |
 |
| 49. Algorithmic Aspects of Machine Learning | 50. The Matrix Cookbook | 51. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit |
|---|---|---|
| By: Ankur Moitra Year: 2014 |
By: Kaare Brandt Petersen and Michael Syskind Pedersen Year: 2012 |
By: Steven Bird, Ewan Klein, and Edward Loper Year: 2009 Publisher: O'REILLY |
| Front Cover Thumbnail Not Found! | Front Cover Thumbnail Not Found! |
| 52. Convex Optimization | 53. A Brief Introduction to Neural Networks | 54. Pattern Recognition and Machine Learning |
|---|---|---|
| By: Stephen Boyd and Lieven Vandenberghe Year: 2009 Publisher: Cambridge University Press |
By: David Kriesel Year: 2007 |
By: Christopher Bishop Year: 2006 Publisher: Springer |
 |
Front Cover Thumbnail Not Found! |  |
| 55. Gaussian Processes for Machine Learning | 56. Information Theory, Inference, and Learning Algorithms | 57. Machine Learning |
|---|---|---|
| By: Carl Edward Rasmussen and Christopher K. I. Williams Year: 2006 Publisher: MIT Press |
By: David MacKay Year: 2003 Publisher: Cambridge University Press |
By: Tom Mitchell Year: 1997 Publisher: McGraw Hill |
 |
 |
 |