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All Blog Posts Tagged 'Dynamical' (5)

IoT Data Science & “DML” – match made in heaven?

DML stands for “Dynamical Machine Learning” (more in the book, “SYSTEMS Analytics for IoT Data Science”, 2017). This match is not surprising once you realize that DML & IoT are both based on the venerable Systems Theory. Let us dig deeper . . .

Consider IoT for industrial applications. A machine is instrumented with sensors, data are collected in real-time (or at intervals), communicated to the cloud where IoT Data Science…

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Added by PG Madhavan on September 11, 2017 at 12:30pm — No Comments

NEXT Machine Learning Paradigm: “DYNAMICAL"​ ML

Dynamical ML is machine learning that can adapt to variations over time; it requires “real-time recursive” learning algorithms and time-varying data models such as the ones described in the blog,…

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Added by PG Madhavan on March 18, 2017 at 2:30pm — 1 Comment

Static & DYNAMICAL Machine Learning – What is the Difference?

 

In an earlier blog, “Need for DYNAMICAL Machine Learning: Bayesian exact recursive estimation”, I introduced the need for Dynamical ML as we now enter the “Walk” stage of “Crawl-Walk-Run” evolution of machine learning. First, I defined Static ML as follows: Given a set of inputs and outputs, find a static map between the two…

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Added by PG Madhavan on October 6, 2016 at 10:04am — No Comments

Generalized Dynamical Machine Learning

In this year of Rudolf Kalman’s demise, this article is dedicated to his memory.

 

We introduce a new Machine Learning (ML) solution for Dynamical, Non-linear, In-Stream Analytics. Clearly, such a solution will accommodate Static, Linear and Offline (or any combination thereof) Machine Learning tasks. The value of such a solution is significant because the same…

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Added by PG Madhavan on September 18, 2016 at 8:00am — No Comments

What exactly is Data Science?

Reading some recent blogs, I sense a level of angst among Data Science practitioners about the nature of their field. What exactly IS Data Science - a question that seems to lurk just below the surface . . .

As a young field of study and work, it will naturally take time for a definition of Data Science to crystallize. In the meantime, see if this works for you . . .…

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Added by PG Madhavan on July 26, 2016 at 3:00pm — 2 Comments

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