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Karla  Yale, Instructor - Robotics Application 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 - Robotics Application 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.

Obtain the real time data exchange from the robot sensors for training AI.

  • Learn how to select the appropriate robot for their application, integrate the real time data exchange at the appropriate frequency and include the predictors that may be needed for training.
  • Prepare the data for machine learning, and deploy robustly for success.
  • Instructors have over 25 years of experience and teach using real world example applications and example code. 

Duration: 2h 51m

Course Description

This course covers data preparation for machine learning. The course includes example applications and example code. Best practices in telecommunication, data cleaning, data transformation, and how to turn the developed algorithms into a robust system are covered in depth. The benefits for taking this course include turning your users and system administrators into heroes, having a system that can remain viable, and successfully moving into the next great application while handing the system to lower level people.

What am I going to get from this course?

Select the appropriate robot for their application, integrate the real time data exchange at the appropriate frequency, include the predictors that may be needed for training, prepare the data for machine learning, and deploy robustly for success.

Prerequisites and Target Audience

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

A knowledge of Python, C++, Kafka, et al, will help if the student really wants to be able to deploy, but these sections can be skipped for the functional user or executive who is interested at the survey level because the class is in the "for dummies" style.

Who should take this course? Who should not?

Because the course is in the "for dummies" style, anyone from functional staff to executives can take the course at the survey level.  To really get to implement these topics, because the underlying math is not covered, a masters in quantitative methods is advised.


Module 1: Introduction

Lecture 1 What this class is

This lecture differentiates between programming robots versus using fully functioning robots for applications.

Module 2: Real time data exchange

Lecture 2 Robots with WiFi

Open source versus closed source robots with telecommunication is covered plus the current status of cellular on board.

Lecture 3 AI applications for robots are discussed.

A robot for machine learning as a class example is selected.

Lecture 4 Test 1

Finding robots with WiFi or cellular on board.

Lecture 5 Real Time Data Exchange

Robots with open source output files.

Lecture 6 Telecommunication

The current state of telecommunication on board robots.

Lecture 7 TC Do's and Don't's

Best practices to avoid pitfalls.

Lecture 8 Example

Selection of an example for class.

Lecture 9 Program platform

Best practices.

Lecture 10 Sample code

Walkthroughs of code.

Lecture 11 Sample CSV file

Walkthrough of variables and format.

Lecture 12 Sample variable list

This list is for the class example application.

Module 3: Two examples

Lecture 13 Sample application 1

Frequency and variables for training the machine learning for application 1.

Lecture 14 Sample application 1

Frequency and variables for training the machine learning for application 2.

Lecture 15 Sample application 2
Lecture 16 Test 2

Submit the frequency and variables for training the machine learning for your application.

Module 4: Automation

Lecture 17 Data collection

Obtaining the samples for training and later for the automated system.

Lecture 18 Cloud storage

Best practices and revisions and upgrades.

Lecture 19 Pseudo real time collection

Real time to your application depends on the frequency of sampling.

Lecture 20 Exogenous factors

The environment defined by your selected predictors may widen over time.

Lecture 21 Data cleaning

Results are only as good as the inputs and your understanding of the application.

Lecture 22 Data transformations

Data usually needs to be transformed so that values which are too big or too small do not skew the results.

Lecture 23 Building the algorithm

The numerous tools available to understand the interactions of the predictors.

Lecture 24 Automation

How to construct the machine learning is covered in more depth in the next course the Industrial Internet of Things, AKA IIoT, AKA Machine to Machine, M2M.

Lecture 25 Machine learning

Neural networks are covered in more depth in the next class, IIoT.

Lecture 26 6 sigma alerts

SPC, AKA Statistical Process Control, is important for being able to manage when something in the predictors goes askew.

Lecture 27 Logging

Logging sampling events is important when some predictor goes askew and needs investigation.

Lecture 28 Reporting

Being able to send logs and reports to the system user on demand or when a predictor goes askew via Android or iOS is key to keeping the system running 24/7.

Module 5: Deploying

Lecture 29 Documentation

Both user and system administrator documentation are important for successful deployment. This can be an AI project inside your machine learning application, LOL.

Lecture 30 Training

Best practices are suggested based on the number of locations in your installation and their geographic proximity to each other.

Lecture 31 Deploying

How to deploy and make your users and system administrators heroes, so that you can move onto your next exciting robot machine learning application.

Lecture 32 Updating

A system that is not updated is not being used and will die from disuse.