Tag: numpy

Classical MLGithub ProjectsMachine Learning
September 18, 2018 Post a Comment

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...

Continue reading
Computer VisionGithub Projects
June 26, 2018 Post a Comment

Camera Calibration

In this project, I use simple manual camera calibation method. The setup is as follows: Assuming pin hole camera, we can utilize similar triangle methods(see below), and can come up with focal lengths equation: # fx = (dx/dX) * dZ , fy = (dy/dY) * dZ where dX, dY are the physical length & width of the object in view and dZ is the distance from object to camera. dx and dy are the corresponding pixel width & height of the object...

Classical MLGithub ProjectsMachine Learning
June 24, 2018 Post a Comment

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....