The “How Machine Learning Works” lecture series concludes by developing some machine learning python code from scratch. We use real valued numbers sampled from two different Gaussians with different priors.
Machine learning technologies have seen many inroads into the advertising industry primarily to make for more intelligent buys and placements in order to deliver a brand message to a selected audience. Here are some compelling SLIDES from a lecture at the New York University Stern School of Business by Foster Provost, Professor of Information Systems: “Machine Learning for Display Advertising.”
The recent Data Day Texas 2014 featured a session given by Josh Wills, Cloudera’s Senior Director of Data Science: “From The Lab To The Factory: Building A Production Machine Learning Infrastructure.”
The “How Machine Learning Works” lecture series continues by building on top of the Bayesian classifier developed in Part 3 of the series. We’ll build an expectation-maximization (EM) algorithm that locally maximizes the likelihood function.
The “How Machine Learning Works” lecture series continues to build on Bayes rule that was taught last time. We’ll define training and testing data sets and build a Bayesian classifier.
The “How Machine Learning Works” lecture series continues by building on fundamental definitions of statistics. This is needed for any rigorous analysis of models or machine learning algorithms.
As social media becomes increasingly important as a data source for the purposes of machine learning, finding a brand new method for analyzing the Twitter microblogging platform is very compelling. Tauid Zaman, assistant professor at MIT’s Sloan School of Management, developed a probabilistic model for the spread of an individual tweet in the twitterverse.
Statistical models that use socio-political data to predict mass atrocities could soon inform governments and NGOs on how and where to take preventative action. The models emerged from one segment of the Tech Challenge for Atrocity Prevention, a competition run by the US Agency for International Development (USAID) and NGO Humanity International.