2013
Coursera / Indian Institute of Technology Delhi
Gautam Shroff + guests
13:33:03
Indian, English
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.
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
Programming: Map-Reduce
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

Download File Size:1.11 GB

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