Posted by on 2017年6月26日

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1.PrimaryAlgorithm List

  • Linear regression (线性回归):
  • Logistic regression(逻辑回归,"评估模型"):
  • Decision tree learning(决定树学习): C4.5/C5.0 and ID3 algorithms.
  • k-Means(k-平均算法): clustering algorithm.
  • SVM(Support Vector Machines,支持向量机):
  • Apriori (先验算法):  rule extraction.
  • EM(Expectation Maximization, 最大期望算法):
  • PageRank(佩奇排名算法): graph-based problems.
  • AdaBoost:  family of boosting ensemble methods.
  • KNN(k-nearest neighbor): instance-based method.
  • Naive Bayes: Simple and robust use of Bayes theorem on data.
  • CART(Classification And Regression Trees): tree-based method.
  • RBFN(Radial Basis Function Network)
  • Random Forest
  • CNN
  • LASSO
  • Logistic
  • MARS
  • Collaborative filtering;
  • (Bonus) Inference via graphical models;
  • Latent factor models based on low-rank matrix factorization like the Singular value decomposition or simple alternating least squares;
  • Bonus: MCMC methods (Markov chain Monte Carlo) for graphical models;

2.Examples of Algorithm Lists To Create Projects (T.B.C.)

Below are 10 examples of machine learning algorithm lists that you could create projects in these days:

  • Regression algorithms
  • SVM algorithms
  • Data projection algorithms
  • Deep learning algorithms
  • Time series forecasting algorithms
  • Rating system algorithms
  • Recommender system algorithms
  • Feature selection algorithms
  • Class imbalance algorithms
  • Decision tree algorithms

 

3.More

下面几篇博文,我觉得写得很好,可以作为梳理ML知识点的map:

Why you should be Spot-Checking Algorithms on your Machine Learning Problems

CRAN Task View: Machine Learning & Statistical Learning

How To Get Started With Machine Learning Algorithms in R by Jason Brownlee

A Tour of Machine Learning Algorithms by Jason Brownlee

Github:Practical Machine Learning

Github:Practical Machine Learning Project

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