some basic algorithm in machine learning including bayes decision rule， gaussian discriminant perceptron, regression, svm, knn, kmeans, neural networks,pca
This project is a homework of machine learning course in Zhejiang University.There are four homework in this project.
In the first homework: hw1
We implement the algorithm of Bayes decision rule, the gaussian discriminant and the text classification using posterior and likelihood we got some result using MLE-maximum likelihood estimation.
In the hw2
We implement some linear models including linear_regression, logistic regression, perceptron and svm. we also show the cross validation of these algorithms.
this is the result of the svm.
In the hw3
We implement KNN, k-means and neural networks including feedforward and backprop algorithm. and there is also a tool written in python to get some verification code image from Zhejiang University website.
this is the result of knn, when k==10.
In the hw4
We write some code to implement Spectral Clustering and pca
this is the result of Sepctral Clustering, when knn_graph==4.
