1800页33章数学方法精要笔记——深入数学建模， 机器学习和深度学习的数学基础

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Front Matter All-in-One

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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