At the "Discovery Summit Japan Online" held in November 2020,
This was a highly requested tutorial session , "Special Tutorial: JMP
We held a two-part series titled "Introduction to Statistical Machine Learning" on Thursday, May 13th and Thursday, May 20th .
The video of this seminar will be available for a limited time until 5:00 p.m. on Tuesday, June 15, 2021 .
*This content is no longer available.
Seminar Title
Special Tutorial: Introduction to Statistical Machine Learning with JMP (2 Parts)
overview
Using JMP (Pro) makes analysis easier than using R, Python, etc.
Although we cannot provide fully customized analysis, we are fully able to accommodate semi-custom orders.
With JMP, it's easy to:
① You can perform analysis with just a mouse, without having to type in commands. ② Sets graphs and statistics. ③ You can record the analysis process in a script. ④ You can output reports that follow the flow of the analysis process. ⑤ It is based on statistical thinking, making it ideal for systematic understanding and learning.
In this report, we will use numerical examples to discuss prediction and classification capabilities using JMP.
Methods used include kernel smoothing, SVM and neuro-classification.
We will also compare it with traditional statistical multivariate analysis to deepen our understanding.
Introducing the lecturer : Mr. Motohisa Hirono
Joined Ricoh Co., Ltd. in 1984. Since then, he has been involved in quality management and reliability management within the company.
Engaged in raising awareness and spreading awareness of statistics.
After serving as the head of the Quality Management Department and the SF Business Center, he is currently the head of the Biomedical Business Center.
As part of the Pharmaceutical Affairs and Quality Assurance Group (ethics committee member) of the Healthcare Business Support Office, he provides education and lectures both inside and outside the company.
It's widespread.
Part-time lecturer at the Faculty of Engineering, Tokyo University of Science (1997-1998) and the Faculty of Policy Management, Keio University (2000-2004).
His main fields of expertise are statistical quality control and reliability engineering. His major publications include "Graphical Modeling in Practice" and
"How to use multivariate data with JMP", "23 lectures on statistical calculation methods using Anscombe-style numerical examples",
His books include "Multivariate Analysis for Engineers using JMP" and "Eye-opening Multivariate Analysis."
Title of each session : Part 1: JMP graph functions useful for big data Part 2: Supervised classification in practice
