Downloads Whole book Front Matter 
 All-in-One Selective topics Linear Algebra and Matrix Analysis 
 Mathematical Optimization 
 Probability and Statistical Estimation 
 Stochastic Process 
 Markov Chain and Random Walk 
 Linear Regression Analysis 
 Statistical Learning 
 Neural Network and Deep Learning 
 (Deep) Reinforcement Learning Table of Contents I Mathematical Foundations Sets, Sequences and Series Metric Space Advanced Calculus Linear Algebra and Matrix Analysis Basic Functional Analysis II Mathematical Optimization Methods Unconstrained Nonlinear Optimization Constrained Nonlinear Optimization Linear Optimization Convex Analysis and Convex Optimization Basic Game Theory III Classical Statistical Methods Probability Theory Statistical Distributions Statistical Estimation Theory Multivariate Statistical Methods Linear Regression Analysis Monte Carlo Methods IV Dynamics Modeling Methods Models and estimation in linear systems Stochastic Process Stochastic Calculus Markov Chain and Random Walk Time Series Analysis V Statistical Learning Methods Supervised Learning Principles and Methods Linear Models for Regression Linear Models for Classification Generative Models K Nearest Neighbors Tree Methods Ensemble and Boosting Methods Unsupervised Statistical Learning Neural Network and Deep Learning VI Optimal Control and Reinforcement Learning Methods Classical Optimal Control Theory Reinforcement Learning Appendix: Supplemental Mathematical Facts github地址： https://github.com/yangyutu/EssentialMath