Stochastic-pattern-recognition

P1: a simple linear regression problem using the basic closed form formula and stochastic gradient descent
P2: binary, one-vs-one and one-vs-all classification with logistic regression, softmax regression
P3: bayesian classification, Naïve bayes classification
P4: kernel density estimation using the following methods: Histogram, Parzen-window, Gaussian kernel and KNN ; and a simple image compression technique using PCA
P5: Kmeans and SVM
P6: Multi‑disease prediction model using improved SVM‑radial bias technique in healthcare monitoring systems