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We live in an era of Big Data, where Science, Engineering and Technology are producing massive data streams, with petabyte and exabyte scales becoming increasingly common. Besides the explosive growth in volume, Big Data also has high velocity, high variety and high uncertainty. These complex data streams require ever-increasing processing speed, economical storage and timely response for decision making in highly uncertain environments and have raised various challenges to the conventional data analysis. With the primary goal of building intelligent systems that automatically improve from experiences, machine learning is becoming an increasingly important field to tackle the Big Data challenges, with the emerging field of Big Learning which covers theories, algorithms and systems on addressing big data problems. Bayesian methods represent one important class of statistical methods for machine learning, with substantial recent developments in adaptive, flexible and scalable Bayesian learning.
Bayesian methods are becoming increasingly relevant in the era of Big Data to protect high capacity models against overfitting and to allow models to adaptively update their capacity. However, their application to Big Data problems create a computational bottleneck that needs to be addressed with new inference methods. Bayesian methods are conceptually simple and flexible hierarchical Bayesian modeling offers a flexible tool for characterizing uncertainty, missing values, latent structures and more. Moreover, regularized Bayesian inference further augments the flexibility by introducing an extra dimension to incorporate domain knowledge or to optimize a learning objective.
This seminar focuses on the mathematical theory and computational tools utilized in Bayesian modeling, inference and machine learning. Applications for Bayesian Statistics will be drawn chiefly from weather and ocean prediction, autonomous vehicles and socio-technical systems. While interdisciplinary training has generally been frowned upon, this seminar acts as a forum for the intersection of Mathematicians and Engineers, thus improving communication among various disciplines.
• Introduction to Bayesian Modeling, Inference and Machine Learning
• Big Data Analytics and its Applications
• Bayesian Methods in Practice: A Big Data Perspective
Dr. S. Chitrakala
Department of Computer Science and Engineering
College of Engineering Guindy Anna University, Chennai
Dr. Deepak N Subramani
Department of Computational and Data Sciences
Indian Institute of Science, Bangalore
Boarding and lodging will be provided for all the participants in the college campus on a chargeable basis. Participants are required to make own arrangements for those who accompany them, if any.
How to reach Kongu Engineering College, Erode
The college is located at Perundurai on the National Highway (NH 47) about 80 kms from Coimbatore and 20 kms from Erode. It is well connected by road & rail.
Event Sponsors in Erode
CSIR, New Delhi
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