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Summary: This course is about building `web-intelligence' applications using `big data'.

Context: Today business intelligence is being replaced by web intelligence, which exploits vast `big' data sources from social media, mobile devices and sensors. At the same time the database world is witnessing an evolution towards newer big-data platforms based on the 'map-reduce' parallel programming paradigm.

Description: The past decade has witnessed the successful of application of many AI techniques used at `web-scale’, on what is are popularly referred to as big data platforms based on the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-SQL databases and stream computing engines. Online advertising, machine translation, natural language understanding, sentiment mining, personalized medicine, and national security are some examples of such AI-based web-intelligence applications that are already in the public eye. Others, though less apparent, impact the operations of large enterprises from sales and marketing to manufacturing and supply chains. In this course we explore some such applications, the AI/statistical techniques that make them possible, along with parallel implementations using map-reduce and related platforms.

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