Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Explainable Machine Learning, Model Transparency, and the Right to Explanation
 Check out this topical video from Predictive Analytics World...
Guidebook to the Future of Data Science: How to Navigate the Increasingly Symbiotic Dynamic Between Executives and Universities
 Book Review of Closing the Analytics Talent Gap: An...
Guilty or Not Guilty: Weight of Evidence
 You have been invited to serve as a juror...
How Machine Learning Works for Social Good
  Originally published in KDnuggets, Nov 2020. This article...

Original Content

“If We Place Graduates Into the Private Sector, We Failed”: Why Universities and Companies Need to Rethink the Role of the PhD

 This is part 4 of a 5-part series on university/corporate partnerships in analytics and data science. In addition to this article, Dr. Priestley will also present on this topic at Predictive Analytics World for Business in Las Vegas, May 31-June 4, 2020. For details about her session, “How Leading Enterprises Leverage Universities to Boost Analytical

Looking At The Numbers in COVID-19

 Like many of you, my focus during this crisis has been less on analytics and more about family, friends, etc. which on a more positive note seems to gain greater emphasis as we reassess our priorities.  But...

Some Thoughts on Analytics in a Post COVID-19 Environment

 In these most difficult times, the use of analytics is certainly not top of mind for most organizations unless it is being used to combat the virus. The challenging scenarios of meeting payroll and having access to...

Re-examining Model Evaluation: The CRISP Approach

 The performance of prediction models can be judged using a variety of methods and metrics. Some years ago, I was challenged to arrive at a set of rules that would provide both the analyst and marketer guidance...

An Agile Approach to Data Science Product Development

 Introduction With the huge growth in machine learning over the past few years, there is a lot of discussion, but few frameworks, on effective AI Project Management. Industry-standard frameworks for data analysis projects, like CRISP-DM, exist but...

Wise Practitioner – Predictive Analytics Interview Series: Haig Nalbantian at Mercer – BIZ

 By: Eric Siegel, Founder, Predictive Analytics World for Business In anticipation of his upcoming conference presentation at Predictive Analytics World for Business Las Vegas, May 31-June 4, 2020, we asked Haig Nalbantian, Senior Partner, Co-leader Mercer Workforce...

Is What You Did Ethical? Helping Students in Computational Disciplines to Think About Ethics

 In addition to this article, Dr. Priestley will also present on this topic at Predictive Analytics World for Business in Las Vegas, May 31-June 4, 2020. For details about her session, “How Leading Enterprises Leverage Universities to...

Wise Practitioner – Predictive Analytics Interview Series: Keith Higdon at ESIS

 By: Eric Siegel, Program Co-Chair, Predictive Analytics World for Financial In anticipation of his upcoming conference presentation at Predictive Analytics World for Financial, May 31-June 4, 2020, we asked Keith Higdon, President, ESIS, a few questions about...

Wise Practitioner – Predictive Analytics Interview Series: Bob Nisbet at University of California, Irvine

  By: Jeff Deal, Conference Chair, Predictive Analytics World for Healthcare In anticipation of his upcoming conference presentation at Predictive Analytics World for Healthcare Las Vegas, May 31-June 4, 2020 we asked Bob Nisbet, Instructor at University...

Predicting the President: Two Ways Election Forecasts Are Misunderstood

  Originally published by Big Think When it’s a presidential election year, speculation is in the cards. It’s the national pastime. Everyone wants to predict who’ll win. But, man, did people mismanage their own expectations leading up...

Page 4 of 60 1 2 3 4 5 6 7 8 9 60