Machine_Learning

Machine Learning by Andrew Ng on Coursera

Ex1 Linear Regression

• $J(\theta)$
• Gradient descent
• $J(\theta)$ with multiple variables
  • Study about learning rate
• Feature Normalization
• Normal Equations

Ex2 Logistic Regression

• plotData.m - Function to plot 2D classification data
• sigmoid.m - Sigmoid Function
• costFunction.m - Logistic Regression Cost Function
• predict.m - Logistic Regression Prediction Function
• costFunctionReg.m - Regularized Logistic Regression Cost

Ex3 Multi-class Classification and Neural Networks

• lrCostFunction.m - Logistic regression cost function
• oneVsAll.m - Train a one-vs-all multi-class classifier
• predictOneVsAll.m - Predict using a one-vs-all multi-class classifier
• predict.m - Neural network prediction function

Ex4 Neural Networks Learning

• sigmoidGradient.m - Compute the gradient of the sigmoid function
• randInitializeWeights.m - Randomly initialize weights
• nnCostFunction.m - Neural network cost function

Ex5 Regularized Linear Regression and Bias v.s. Variance

• linearRegCostFunction.m - Regularized linear regression cost function
• learningCurve.m - Generates a learning curve
• polyFeatures.m - Maps data into polynomial feature space
• validationCurve.m - Generates a cross validation curve