Hi everyone,
In August 2020 we began creating a list of recommended resources for sales teams to consider in post-event follow-up with customers and prospects. These curated lists pull appropriate assets from across our web site, including white papers, articles, book chapters, webinars, JMP On Air episodes, blogs, STIPS modules and more. Any asset that makes a list is considered relevant to the topic, application area or industry, performs well and is recent enough to share. Still, to help distinguish between resources, I also include a short summary, a few words of commentary and a 4-star system to identify the "best of the best." (You can also find all past resource recommendations here.)
Immediately below, you'll find several great JMP resources to include with your follow up for our most recent event, the 01Oct2020 Statistically Speaking webinar, Demystifying Machine Learning and AI. (After the event, you'll find the recording in the "on-demand" section on the Stat Speaking Landing Page.) Please use these resources selectively (there are probably too many to position them all) and tailor as needed or to best address your audience's pain points.
If you have any questions, please let me know.
Thanks
Larry
x11276
Sales Force Campaign Link (Live Stream) Found Here
Sales Force Campaign Link (On-Demand) Found Here
October 1, Demystifying Machine Learning and AI Recommendations
4 Stars Expert guidance on making the most of machine learning, Stat Speaking Event
Includes Dick DeVeaux keynote, The Seven Deadly Sins of Machine Learning. Excellent panel presentation with Dick, Russ Wolfinger and Julia O’Neill
4 Stars Solving science and engineering problems with machine learning, Stat Speaking Event
Very strong resource. European based speakers, but in-depth discussion (keynote) on deep learning, AI and strong/engaging panel.
4 Stars What is Machine Learning (and can JMP do it), JMP Blog
1st part of Elizabeth Claassen’s blog series. Good introductory discussion of concepts around machine learning and outlines all the different machine learning algorithms JMP can do. Promotes the free trial.
3 Stars, Dark Data, Book Chapter
Though the topic is not a perfect match, this book chapter is from David Hand, who is the plenary speaker of this event. Dark Data is Hand’s newest book. (David is the keynote speaker for this webinar) The chapter talks about missing data or data not known and the effect it can have on analysis. Along the way, Hand discusses data phenomena like p-hacking, spurious measurement errors, and false discovery rates, and “HARKing.”
3 Stars, Mission-Critical Advanced Statistics for the Defense Community, Statistically Speaking
Though this event was specific to the defense industry, it does cover concepts that would apply across industries and provides a baseline understanding of AI. During the keynote, Laura Freeman discusses how they’re using statistical thinking to use machine learning and AI in a way that helps the Department of Defense deliver on key missions, up-skill their workforce and ensure success.
3 Stars Mission-Oriented Analytics: New Methods for the Intelligence Community, Analytically Speaking
Industry connection, Alyson Wilson, NCSU, works on bringing analytics more into the intelligence community to influence foreign policy and national security. Also a good discussion on differences between machine learning and AI. 32 views in 2020, not popular.
3 Stars Applying machine learning: Will I beat my husband at pool on my birthday? JMP Blog
2nd part of Elizabeth Claassen’s blog series. Uses a fun example of machine learning algorithm. Like the connections this blog makes. Think it does a good job of humanizing our experts and our business.
2 Stars Smart Machine Learning for Manufacturers, White Paper
Not a perfect match because it’s manufacturing example, but interesting discussions around merging machine learning with statistician knowledge – human and machine.
1 Star Machine Learning Drives Innovation in High-Tech Manufacturing, Customer Success Story
Not a strong asset because it’s the semiconductor industry, but does provide an example of machine learning implementation. It’s also a pretty surface level discussion, but it does get decent traffic and is featured on our success story home page.
Additional suggestions from Peter Hersh:
Data Mining and Predictive Modeling, JMP Tutorial
This JMP Tutorial includes a manufacturing case study and includes demonstrations of a number of techniques using Graph Builder and the Prediction Profiler and JMP Pro. Discussions on topics related to machine learning and AI, including regression, Bootstrap Forest and Boosted Trees (Decision Trees) and Neural Networks.
These sessions also include discussions on tree-based models and neural networks:
Understanding and Applying Tree-Based Methods for Predictor Screening and Modeling, Mastering JMP Webinar
Discovering and Predicting Patterns Using Neural Network Models, Mastering JMP Webinar
Totally agree with Rebecca, this information is extremely helpful!!
