Coursera

This repository contains some of the homework I did for coursera Machine Learning Course.

My codes can be found in the following files.

Exercise 1

1. warmUpExercise.m - Simple example function in Octave/MATLAB
2. plotData.m - Function to display the dataset
3. computeCost.m - Function to compute the cost of linear regression
4. gradientDescent.m - Function to run gradient descent

Exercise 2

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

Exercise 3

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

Exercise 4

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

Exercise 5

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

Exercise 6

1. gaussianKernel.m - Gaussian kernel for SVM
2. dataset3Params.m - Parameters to use for Dataset 3
3. processEmail.m - Email preprocessing
4. emailFeatures.m - Feature extraction from emails

Exercise 7

1. pca.m - Perform principal component analysis
2. projectData.m - Projects a data set into a lower dimensional space
3. recoverData.m - Recovers the original data from the projection
4. findClosestCentroids.m-Findclosestcentroids(usedinK-means)
5. computeCentroids.m - Compute centroid means (used in K-means)
6. kMeansInitCentroids.m - Initialization for K-means centroids

Exercise 8

1. estimateGaussian.m - Estimate the parameters of a Gaussian dis- tribution with a diagonal covariance matrix
2. selectThreshold.m - Find a threshold for anomaly detection
3. cofiCostFunc.m - Implement the cost function for collaborative fil- tering