Presentation Slides: Machine Learning, Predictive Modelling, and Pattern Recognition in Business Analytics
April 13, 2013 Leave a comment
I recently delivered a presentation entitled “Using Advanced Predictive Modelling and Pattern Recognition in Business Analytics” at the Statistical Society of Canada’s (SSC’s) Southern Ontario Regional Association (SORA) Business Analytics Seminar Series. In this presentation, I
– discussed how traditional statistical techniques often fail in analyzing large data sets
– defined and described machine learning, supervised learning, unsupervised learning, and the many classes of techniques within these fields, as well as common examples in business analytics to illustrate these concepts
– introduced partial least squares regression and bootstrap forest (or random forest) as two examples of supervised learning (0r predictive modelling) techniques that can effectively overcome the common failures of traditional statistical techniques and can be easily implemented in JMP
– illustrated how partial least squares regression and bootstrap forest were successfully used to solve some major problems for 2 different clients at Predictum, where I currently work as a statistician
This presentation was aimed at a broad audience of academic statisticians, industrial statisticians, business analysts, executives, managers, and students, so I presented the statistical and machine learning concepts with a moderate amount of technicality – enough to give an intuitive idea and the essence of the models, but without any deep mathematics. These slides should be fairly understandable to anyone with an introductory understanding of statistics or data analysis.
These slides do not capture the live demonstrations that I gave on how to implement the above techniques in JMP, a powerful point-and-click statistical software that is especially useful for generating insightful visualizations of data and easily readable reports. JMP is notable for its comprehensive suite of platforms, ranging from basic data analysis and descriptive statistics to advanced techniques in machine learning, time series analysis, and experimental design. Predictum is a leading user of JMP and regularly provides training on using JMP for statistics and analytics. We also have some top-notch JMP Scripting Language (JSL) programmers, which enables us to write customized software to suit our clients individual needs in statistics and analytics in JMP. Thus, if you are interested in learning more about how JMP can be used to implement techniques in machine learning or any other statistical field, please visit our web site and contact us.
The slides for my seminar can be found on the web site for the SORA Business Analytics Seminar Series, which features upcoming seminars as well as archiving any relevant materials from previous seminars.
My thanks to those who attended my seminar.