Using Computer with your Statistics Major Course
Undergraduate-in-Statistics
Original Name: Using-X-Series The idea of using the new name is that, I am trying to combine all the course during the statistsics's bacholar period, not only using python, R or MATLAB, in fact this programming language are all important
Also, Happy 2020, I will try to finish this project before the end of 2020! (not finished, and continue in 2021)
What's more, I have removed the topic Data Mining, I thought there are multiple name in this area, "Data Mining", "Data Analysis", "Machine Learning", "Statistical Learning". Overall, there are very similar with tiny differences. In the new version, I would like to use "Statistical Learning" instead of other name.
In this series, I will try to teach the basic knowledge in Calculus, Linear Algebra, Advanced statistics and etc., which are required courses in UIC.
In these articles, most of the time, I will use numpy, sumpy and matplotlib. We can use numpy and scipy to deal with the high dimensional array or the matrix operation easily. With matplotlib, we can visualize the data and this is more intuitionistic.
Calculus
- What is Function
- Composition
- Euler's Formula
- Limits
- Derivative
- Newton's Method
- Optimization
- Integration and Differentiation
- Ordinary Differential Equations,ODE)
Linear Algebra
- Chapter Zero multiplication in LA and the lib we use
- Chapter One Matrix
- Chapter Two Determinant
- Chapter Three Vector
- Chapter Four Vector Space
- Chapter Five Linear Algebra Advanced Text
Advanced Statistics(Probability and Statistics)
- Chapter One Probability
- Chapter Two Statistics
Bayesian Statistics
- Chapter Zero Review
- Chapter One Introduction
R language as auxiliary material. I am now looking for Bayes theorem related python, Think Bayes too simple)
Statistical Learning
This repo is writen with jupyter notebook
Who is suitable for this lesson? Those who are interested in both statistics and python*
Reference
用 python 学微积分>
机器学习的数学基础:矩阵篇>
机器学习的数学基础:向量篇>
机器学习的数学基础:线性代数进阶篇>
Python-for-Probability-Statistics-and-Machine-Learning>
Think bayes>
统计分布 [Statistical Distribution] Written by Prof.Kai Tai Fang, Prof.Jian Lun Xu>
概率论与数理统计 [Probailities and Statistics] Written by Prof Xi Ru Chen>
Linear Regression in Python with Scikit-Learn>
Introduction to Data Mining>
Introduction to Statsitcal Learning with R aka ISLR
Contributors
Timothy Wu put forward a amendments: `1. Higher order function应为 composite function复合函数;
2. Big O 那段写的不是很清楚,其实Big O主要是表示算法的计算复杂度,微积分里面用的不多;
3. 切线前面可以介绍割线,再用极限的概念引入切线;
4. 可加入包括原函数、一阶导和二阶导(或更高阶导)图像的图;
5. 常微分方程是比不定积分更“高级”的概念,最好使用微积分基本定理引入不定积分;
6. 可以加入曲线下(间)面积、黎曼和和定积分的关系;
7. 可以加入求旋转体的体积作为积分的应用
Wiki
中文版文檔請看: READMECNIf you like it, you can buy me a coffee!
