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…

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

** 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,…

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

Many people worry that "AI" will usher in a new Industrial revolution where machines replace humans. My take is that it will be more like the Printing press revolution that launched the Age of Enlightenment! The effect will be less of soaring productivity but more of better decision-making leading to a SMARTER society.

Part of the problem is the misnomer, "AI or artificial intelligence"…

ContinueAdded by PG Madhavan on November 2, 2016 at 12:00pm — No Comments

** **

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…

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…

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

As a new sub-discipline of Data Science, I notice that SYSTEMS Analytics is starting to get some traction! There are a couple of Analytics graduate level programs with *Systems* in its title (Stevens Institute of Technology and University of North Carolina are the only ones I know). Web search brings up NO books on *Systems* Analytics. With the publication of my book with *Systems* in the title, that gap has been filled now! “…

ContinueAdded by PG Madhavan on August 5, 2016 at 9:00am — 4 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 . . .…

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

In my recent blog, Marrying Kalman Filtering & Machine Learning, we saw the merger of *Bayesian exact recursive estimation* (algorithm for which is Kalman Filter/Smoother in the linear, Gaussian case) and *Machine Learning*. We developed a solution called **Kernel Projection Kalman Filter** for business applications that…

Added by PG Madhavan on July 21, 2016 at 2:06pm — 1 Comment

Machine Learning today tends to be “open-loop” – collect tons of data offline, process them in batches and generate insights for eventual action. There is an emerging category of ML business use cases that are called “In-Stream Analytics (ISA)”. Here, the data is processed as soon as it arrives and insights are generated quickly. However, action may be taken offline and *the effects of the actions are not immediately incorporated back into the learning process*. If we did, it is an…

Added by PG Madhavan on May 20, 2016 at 5:30am — No Comments

An insightful person once said, “Prediction is like driving your car forward by looking only at the rearview mirror!”. If the road is dead-straight, you are good . . . *UNLESS there is a stalled vehicle ahead in the middle of the road.*

We should consider short-term and long-term prediction separately. Long-term prediction is nearly a lost cause. In the 80’s and 90’s, chaos and complexity theorists showed us that things can spin out of control even when we have perfect…

ContinueAdded by PG Madhavan on January 26, 2016 at 2:08pm — No Comments

- IoT Data Science & “DML” – match made in heaven?
- NEXT Machine Learning Paradigm: “DYNAMICAL" ML
- IoT Machine Learning – Industrial or Printing Press revolution?
- Static & DYNAMICAL Machine Learning – What is the Difference?
- Generalized Dynamical Machine Learning
- Introducing SYSTEMS Analytics
- What exactly is Data Science?

- NEXT Machine Learning Paradigm: “DYNAMICAL" ML
- Generalized Dynamical Machine Learning
- Need for DYNAMICAL Machine Learning: Bayesian exact recursive estimation
- What exactly is Data Science?
- Introducing SYSTEMS Analytics
- ADAPTIVE Machine Learning
- Static & DYNAMICAL Machine Learning – What is the Difference?

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