Programming Exercise 2 in Machine Learning course by Andrew Ng on Coursera.
The course info can be found here https://class.coursera.org/ml-004/class/index.
In this exercise, a logistic regression model to predict whether
a student gets admitted into a university will be created step by step.
The details of this assignment is described in ex2.pdf
The codes are written by Octave.
For further info of Octave please see http://www.gnu.org/software/octave/
This set includes:
ex2.m - Octave script that will help step you through the exercise
ex2_reg.m - Octave script for the later parts of the exercise
ex2data1.txt - Training set for the first half of the exercise
ex2data2.txt - Training set for the second half of the exercise
mapFeature.m - Function to generate polynomial features
plotDecisionBounday.m - Function to plot classifier’s decision boundary
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