Gaussian Mixture Models

In this project we explore the Gaussian Mixture models. GMMs are universal approximators. This means that any probability density can be approximated to arbitrary precision using mixture of gaussian densities. We saw a glimpse of it in our Hypothesis Testing II project. GMMs are go-to models for unsupervised learning schemes and one of my favorite ML models. In fact, they utilize (usually) EM algorithm, which is an iterative method of estimating statistical parameters similar to and I belive, on par with, backpropagations in Neural nets. In this...

Hypothesis Testing III – Bayesian Methods

This project is third in the series of Hypothesis testing project whereby we use Bayesian Methods. Here we reformulate the AB testing problem into MultiArm Bandit and implement 4 widely used algorithms: Epsilon Greedy, Optimistic Initial Conditions, UCB1, and Bayesian (Thompson) Sampling for the AB testing. We then compare and contrast the performance of the algorithms wrt the Bandit problem....

Support Vectors

In this project we utilize SVM to evaluate ML performanceson IRIS image dataset. The Data: Iris flower data set. The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor), so 150 total samples. Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters. Here's a picture of the three different Iris types:...