Machine and Deep Learning for Data Fusion


Subrata Das

Machine Analytics, Inc.

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Duration:  1-2 days

In this short course, I will present some techniques for fusion and analytics to process big centralized warehouse data, inherently distributed data, and data residing on the cloud. The broad range of artificial intelligence and machine and deep learning techniques to be discussed will handle both structured transactional and sensor data as well as unstructured textual data such as human intelligence, emails, blogs, surveys, etc., and image data. Specifically, the Short Course will explore Deep Fusion to solve multi-sensor big data fusion problems applying deep learning and artificial intelligence technologies.

As a background, this short course is intended to provide an account of both the cutting-edge and the most commonly used approaches to high-level data fusion and predictive and text analytics. The demos to be presented are in the areas of distributed search and situation assessment, information extraction and classification, and sentiment analyses. There will be some hands-on exercises.

Some of the short course materials are based on the following two books by the speaker: 1) Subrata Das. (2008). “High-Level Data Fusion,” Artech House, Norwell, MA; and 2) Subrata Das. (2014). “Computational Business Analytics,” Chapman & Hall/CRC Press.

Topics include the following: High-Level Fusion, Traditional Machine Learning Algorithms, Popular Deep Learning Algorithms (e.g. Convolutional & Recursive Neural Networks, Deep Belief Networks and Restricted Boltzmann Machine, Stacked Autoencoder, ResNet, LSTM), Bagging and Boosting, Descriptive and Predictive Analytics, Text Analytics, Decision Support and Prescriptive Analytics, Cloud Computing, Distributed Fusion, Hadoop and MapReduce, Natural Language Query, Graphical Probabilistic Models, Bayesian Belief Networks, Text Classification, Supervised and Unsupervised Classification, Information Extraction, Natural Language Processing, Demos in R and Python.

Overall objective and learning outcomes of the Short Course: Prepare students, researchers, and industry practitioners with cutting-edge tools and technologies to face the new wave of data science challenges for data fusion.

Intended Audience: The intended audience include designers and developers of analytics systems for any vertical (e.g., defense, healthcare, finance and accounting, human resources, customer support, transportation) who work within business organizations around the world. They will find the course useful as a vehicle for moving towards a new generation of big data fusion and analytics approaches.