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Instructors
Karla  Yale, Instructor - IIoT Applications for Machine Learning

Karla Yale

Karla Yale has over 25 years of experience and has held an MBA from Kellogg with double major in quantitative methods and operations management. Postions as Sr VP GE Capital Markets; Director IT Continental Can; Manufacturing Engineering Consulting Cummins Corporate do not begin to describe her exciting career designing and deploying prediction systems for McDonald’s, Wurlitzer, Borg Warner, GE Wiring, GE Refrigerator, GE Motors and Drives, GE Nuclear, GE and Parker Hannifin Aerospace, Continental Can SPC, as well as control systems for Perkin Elmer Research, IBM Components, Bell Labs, Amoco Chemicals, Standard Oil of Indiana Research, US Steel, Continental Can, Cummins.
Randy Barnes, Instructor - IIoT Applications for Machine Learning

Randy Barnes

Randy Barnes currently works as an engineering consultant for Firehouse Consulting. His has previously worked with Modular Controls in cartridge valve design, Neff Engineering in control systems engineering, Danaher Corporation in sensor development and sales, President, Alert Technologies, in manufacturing of leak detection systems. He studied Physics at Indiana University and advanced studies in hydrodynamics at Milwaukee School of Engineering with US Naval Avionics training.

Instructors: Karla Yale,  Randy Barnes

Learn the elements of a robust IIoT control system through applications.

  • Learn the elements of a robust IIoT control system through applications.
  • Know how to obtain the data via real time data exchange and telecommunications.
  • Instructors have over four decades of experience and teach using real world example applications and example code. 

Duration: 46m

Course Description

This IIoT for Machine Learning lecture series gives examples, walks through code, and focuses heavily on machine learning.

What am I going to get from this course?

Think of an Industrial Internet of Things application which will benefit their organization of employment, know how to obtain the data via real time data exchange and telecommunications, understand the code involved, remember the importance of data preparation from the prerequisite class, be introduced to algorithm building, automate the algorithm(s), and build and deploy the predictive system.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

The prerequisite course is the Robot Applications for Machine Learning course because that course covers how to select technology and data preparation topics in much more detail.  This course covers neural networks in greater detail.

Who should take this course? Who should not?

This course is aimed for implementers, but is helpful for executives who need to know what is involved for budgetary and scheduling purposes.  Executives can choose to skip the code walk throughs.

Curriculum

Module 1: Introduction

05:38
Lecture 1 Introduction
00:36

Who your instructors are.

Lecture 2 Entire course outline
05:02

Top level review of what is covered in 29 lectures.

Module 2: Application Examples.

06:20
Lecture 3 Applications
06:20

Chemical, paint, and oil industry applications are covered.

Module 3: Real Time Data Exchange

31:42
Lecture 4 Real Time Data Exchange
00:53

Topics are sensors with WiFi

Quiz 1 Test 1

https://drive.google.com/drive/u/0/folders/0B-cBQwBR1spfUm9KTWJZNjV5TWM M2MIV.avi

Lecture 5 Real Time Data Exchange
23:12

Actual code walk through.

Lecture 6 Program Platform
02:07

Best choices for hardware and software.

Lecture 7 Sample code
02:14

Refers to the code walk through in lecture 5 of this module.

Lecture 8 Sample Comma Separated Variable File
01:49

The power is in the details.

Lecture 9 Sample Variable List
00:36

This will change for any specific application.

Lecture 10 Sample Application 1
00:17

Sample frequency and variables are presented.

Lecture 11 Sample Application 2
00:34

Frequency of sampling and determination of predictive variables are covered.

Quiz 2 Test 2

Start thinking further about your application.

Module 4: Telecommunication

02:41
Lecture 12 Best practices
02:03

Best practices, do's and don't's, and code walk through.

Lecture 13 Telecommunication Do's and Don't's
00:38

Avoid the following pitfalls.

Resource 1 Example

Walk through of code which becomes firmware on board for controlling telecommunication.

Module 5: Machine Learning

Resource 2 Data Preparation

Follow these steps to giving your predictive model(s) a good basis for learning.

Resource 3 Building Algorithm

Tips and tools.

Resource 4 Automation

Tips for saving time during machine learning for initial model and also for implementation for deployment and installation.

Resource 5 Machine Learning

At least 10 different neural network functions are presented along with some code examples/

Module 6: Deploying

Resource 6 Six Sigma Alerts

These alerts are file driven by user so that each user can determine based on their responsibilities, if they want amber, orange, or red alerts or some combination thereof.

Resource 7 Logging

The log files are always written because they provide a clue as to what is skewing in the model or the hardware usage.

Resource 8 Reporting

Like logging, the reports are always written to a file which can be accessed with the proper codes for information based on either timing and/or alerts.

Resource 9 Documentation

Documentation is of two type, one for the systems administrator and the other for the user.

Resource 10 Training

Training strategies are discussed.

Resource 11 Deploying

Deployment strategies are discussed.

Resource 12 Updating

A system that is not getting requests for updates is not being used.

Lecture 14

Module 7: Course Evaluation

Resource 13 Feedback to Instructor Form

Please fill this out.

Resource 14 Instructor Contact Information

Where to send the evaluation form.

Module 8: References

Resource 15
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