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! "SYSTEMS Analytics: Adaptive Machine Learning workbook". My last Analytics startup launched in 2013 explicitly used SYSTEMS Analytics in our Retail Recommendation and Uplift SaaS product; my initial bias for the Systems approach was confirmed by the success of our product.

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 techniques predict machine condition which results in an action, if necessary, such as repair action on the machine.

When you Google "Kalman Filter AND Machine Learning", very few interesting references pop up! Perhaps my search terms are not the best, perhaps Fintech guys keep such algorithms close to their vests, perhaps there is not much of work done in bringing these two incredibly powerful tools together... In any case, Part II of my new book, "Systems Analytics: Adaptive Machine Learning workbook" focuses exactly on this merger. I am happy to report that pre-publication copy of my book (including MATLAB code) is available for download for FREE. See the end of this blog for how . . .

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, Generalized Dynamical Machine Learning. In the process of DYNAMICAL machine learning (DML) applied to industrial IoT, the data model and the algorithms used (Generalized Dynamical Machine Learning) naturally generates what is called the "State-space" model of the machine. It may not *look* like the machine but it captures the dynamics in all its detail (there can be challenges in relating "states" to actual machine components though). I am a proponent of using the "State-space representation" that we get for FREE in Dynamical ML as the "digital twin". This is a topic of current exploration and advancement.

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, Generalized Dynamical Machine Learning. In the process of DYNAMICAL machine learning (DML) applied to industrial IoT, the data model and the algorithms used (Generalized Dynamical Machine Learning) naturally generates what is called the "State-space" model of the machine. It may not *look* like the machine but it captures the dynamics in all its detail (there can be challenges in relating "states" to actual machine components though). I am a proponent of using the "State-space representation" that we get for FREE in Dynamical ML as the "digital twin". This is a topic of current exploration and advancement.

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! "SYSTEMS Analytics: Adaptive Machine Learning workbook". My last Analytics startup launched in 2013 explicitly used SYSTEMS Analytics in our Retail Recommendation and Uplift SaaS product; my initial bias for the Systems approach was confirmed by the success of our product.

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! "SYSTEMS Analytics: Adaptive Machine Learning workbook". My last Analytics startup launched in 2013 explicitly used SYSTEMS Analytics in our Retail Recommendation and Uplift SaaS product; my initial bias for the Systems approach was confirmed by the success of our product.

When you Google "Kalman Filter AND Machine Learning", very few interesting references pop up! Perhaps my search terms are not the best, perhaps Fintech guys keep such algorithms close to their vests, perhaps there is not much of work done in bringing these two incredibly powerful tools together... In any case, Part II of my new book, "Systems Analytics: Adaptive Machine Learning workbook" focuses exactly on this merger. I am happy to report that pre-publication copy of my book (including MATLAB code) is available for download for FREE. See the end of this blog for how . . .