supervised/reinforcement learning

Temporal difference learning

Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, and perform updates based on current estimates, like dynamic programming methods.[1]

https://en.wikipedia.org/wiki/Temporal_difference_learning

linear regression

Multiple Dimensions: Gradients

Stochastic gradient descent(随机梯度下降)

线性回归在不同分组上的效果

AdaBoost(Adaptive Boosting)算法

coordinate descent

Generative Learning Method

logistic regression modeled as where g is the sigmoid function

生成式学习算法的核心思想

The Gaussian Discriminant Analysis model(GDA)

Gaussian Discriminant Analysis - GeeksforGeeks

A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.

https://www.geeksforgeeks.org/gaussian-discriminant-analysis/

机器学习笔记-距离度量与相似度(二)马氏距离 - WarningMessage - 博客园

马氏距离（Mahalanobis Distance） 马氏距离(Mahalanobis Distance)是由印度统计学家马哈拉诺比斯（P. C. Mahalanobis）提出的，表示数据的协方差距离。它是一种有效的计算两个未知样本集的相似度的方法。它考虑到数据特征之间的联系，并且是尺度无关的（sc

https://www.cnblogs.com/dataanaly/p/12875101.html

Gaussian Naïve Bayes

PCA, Matrix Completion, Autoencoders

clustering(聚类)

k-means clusters

Mixtures of Gaussian

EM algrithoms

convex optimization

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