 | Machine Learning a Probabilistic Perspective, Murphy |
 | The Nature of Statistical Learning, Vapnik:This book is contains an excellent description of what "learning" really is in the context of machine learning. It also describes SVM in detail. |
Elements of Information Theory, Cover | |
 | An Introduction to Machine Learning, Kubat |
 | Pattern Recognition and Machine Learning, Bishop |
 | Information Theory, Inference and Learning Algorithms, MacKay |
Machine Learning, Mello | |
 | The Elements of Statistical Learning, Hastie |
 | Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Schwartz |
 | Neural Networks and Deep Learning, Aggarwal |
 | Principles of Deep Learning Theory, Roberts and Yaida |
 | Reinforcement Learning, Sutton |
 | Data-Driven Science and Engineering, Brunton |
 | Kalman Filtering and Neural Networks, Haykin |
 | Data Mining and Analysis, Zaki |
 | Cluster Analysis, Everit |
 | Linear Algebra and Optimization for Machine Learning, Aggarwal |
Algebra, Topology, Differencial Calculus, and Optimization Theory for Computer Science and Machine Learning, Gallier | |
Introduction to Applied Linear Algebra, Stephen Boyd | |
Mathematics for Machine Learning, Garret Thomas | |
 | Mathematics for Machine Learning, Deisenroth |
 | Algorithms for Convex Optimization, Vishnoi |
Course notes on Optimization for Machine Learning, Gabriel Peyré | |
Linear Algebra for Computer Vision, Robotics, and Machine Learning, Gallier |
 | Nonparametric Statistical Methods, Hollander |
 | Applied Regression Analysis, Draper |
 | Mathematics for Machine Learning, Garret Thomas |
| Mathematics for Machine Learning, Deisenroth |
| Algorithms for Convex Optimization, Vishnoi |