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…Continue
Added by PG Madhavan on September 11, 2017 at 12:30pm — No Comments
; it requires “real-time recursive” learning algorithms and time-varying data models such as the ones described in the blog,…Continue
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…Continue
Added by PG Madhavan on October 6, 2016 at 10:04am — No Comments
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…Continue
Added by PG Madhavan on September 18, 2016 at 8:00am — No Comments
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 . . .…Continue