The past decade has witnessed the successful of application of many AI techniques used at `web-scale, on what 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.
This course was offered thrice during Fall 2012, Spring 2012 and Fall 2013; in Fall of both years it was also taken for credit at IIT Delhi and IIIT Delhi. During this period, I also wrote a book to elucidate the ideas discussed in the course at a 'popular' level:
The Intelligent Web: Search, Smart Algorithms and Big Data published by Oxford University Press, UK, in November 2013.
Now in this edition, the course is being offered in 'self-study' mode.
Table of contents
Introduction and Overview
Look: Search, Indexing and Memory
Listen: Streams, Information and Language, Analyzing Sentiment and Intent
Load: Databases and their Evolution, Big data Technology and Trends
Learn: Classification, Clustering, and Mining, Information Extraction
Connect: Reasoning: Logic and its Limits, Dealing with Uncertainty
Programming: Bayesian Inference for Medical Diagnostics
Predict: Forecasting, Neural Models, Deep Learning, and Research Topics
Data Analysis: Regression and Feature Selection
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