This project implements different classiffcation methods and compare its performance in classifying handwritten digits. The following objectives are met:

� How Neural Network works and use Feed Forward, Back Propagation to implement Neural Network

� How K-Nearest Neighbors can be used for classi cation task

The dataset for handwritten digits is taken from http://www.cs.nyu.edu/~roweis/data.html The MNIST dataset consists of a training set of 60000 examples and test set of 10000 examples. All digits have been size-normalized and centered in a xed image of 28�28 size. In original dataset, each pixel in the image is represented by an integer between 0 and 255, where 0 is black, 255 is white and anything between represents different shade of gray.

� preprocess.m: performs some preprocess tasks, and outputs the preprocessed train, validation and test data with their corresponding labels (1 of k encoding scheme for error calculation)

� script.m: Main Matlab script that calls other functions (Neural network implementation and KNN)

� sigmoid.m: Calculates sigmoid

� nnObjFucntion.m: computes the error function of Neural Network.

� nnPredict.m: predicts the label of data given the parameters of Neural Network.

� initializeWeights.m: return the random weights for Neural Network given the number of node in the input layer and output layer.

� fmincg.m: perform optimization task by using conjugate gradient descent (Used as is)

� knnPredict.m: knnPredict predicts the label of given data by using k-nearest neighbor classi cation algorithm.