This is the study notes on Machine Learning class(cs229) in Stanford. I also refer the materials from the class CS6140 in Northeastern University to help me implement algorithms.

• Naive Bayes

  • Two-class NB with/without Missing Features (data: spambase)
  • Multi-class NB with Missing Features (data: censusIncome)

• Linear Regression (data: spambase)

  • Linear Batch Gradient Descent (Regularized & Nomal)
  • Linear Stochastic Gradient Descent
  • Linear Normal Equations (Regularized & Nomal)
  • Locally Weighted Linear Regression (Ref: Chapter 8)

• Logistic Regression (data: spambase)

  • Logistic Batch Gradient Descent (Regularized & Nomal)
  • Logistic Stochastic Gradient Descent
  • Smooth Stochastic Gradient Descent (Ref: (stocGradAscent1))
  • Newton-Raphson(Ref: Logistic Regression by Jia Li)

• Perceptron Algorithm (data: spambase)

• Support Vector Machine

  • Sequential minimal optimization(SMO)

• Neural Network

  • [Encode/Decode] (./code/neuralNetwork/neural_network.py)

• Classification Tree (data: spambase)

  • Decision Tree

• K-Means (toy dataset)

• DBSCAN (toy dataset)

• EM for Mix-Gaussians (toy dataset)

• Principal Component Analysis (PCA) ----- [Note in Latex or PDF]

• Linear Discriminant Analysis (LDA) ----- [Note in Latex or PDF]

• Support Vector Machine (SVM) ----- [Note in Latex or PDF]