  Over the last two decades, we have seen extensive industrial research in the
area of computational advertising. In this paper, our goal is to study the
performance of various online learning algorithms to identify and display the
best ads/offers with the highest conversion rates to web users. We formulate
our ad-selection problem as a Multi-Armed Bandit problem which is a classical
paradigm in Machine Learning. We have been applying machine learning, data
mining, probability, and statistics to analyze big data in the ad-tech space
and devise efficient ad selection strategies. This article highlights some of
our findings in the area of computational advertising from 2011 to 2015.
