AI-Machine-Learning

• Machine Learning here refers specifically to traditional machine learning algorithm(not including Deep Learning)
• Use Jupyter Notebook to study Machine Learning.
• As for each Algorithm, algorithm principle and code will be put together in a Jupyter Notebook in src file.
• Data used in Code will be put in data file.
• Operation results will be put in results file.
• Documents sucha as references are put in doc file.

1. Machine Learning Types

2. Algorithm Outline

Unsupervised Learning

• Gaussian mixture models
• Mainfold learning
• Clustering
• Biclustering
• Decomposing signals in components
• Covariance estimation
• Novelty and Outlier Detection
• Density Estimation

Semi-supervised Learning

• TBD

Supervised Learning

• Linear Models
  • Logistics Regression
• Linear and Quadratic Discriminat Analysis
• Kernel Ridge Regression
• Support Vector Machines
• Stochastic Gradient Descent
• Nearest Neighbors
  • KNN
• Gaussian Processes
• Cross Decomposition
• Naive Bayes
• Decision Trees
  • ID3
  • GBDT
• Ensemble methods
  • Random Forest
• Multiclass and multioutput algorithms
• Feature selection
• Isotonic Regression
• Probability Calibration

Reinforced Learning

• TBD