• The InfoQ Podcast: Cathy O'Neil on Pernicious Machine Learning Algorithms and How to Audit Them

    by Charles Humble on  Sep 16, 2016

    In this week's podcast InfoQ’s editor-in-chief Charles Humble talks to Data Scientist Cathy O’Neil.  Topics discussed include her book “Weapons of Math Destruction,” predictive policing models, the teacher value added model, approaches to auditing algorithms and whether government regulation of the field is needed.

  • Spark GraphX in Action Book Review and Interview

    by Srini Penchikala on  Sep 12, 2016

    “Spark GraphX in Action” book from Manning Publications, authored by Michael Malak and Robin East, provides a tutorial based coverage of Spark GraphX, the graph data processing library from Apache Spark framework. InfoQ spoke with authors about the book and Spark GraphX library as well as overall Spark framework and what's coming up in the area of graph data processing and analytics.

  • Book Review: Cathy O’Neil’s Weapons of Math Destruction

    by Charles Humble on  Sep 06, 2016 4

    “Big Data has plenty of evangelists, but I’m not one of them,” writes Cathy O’Neil, a blogger ( and former quantitative analyst at the hedge fund DE Shaw who became sufficiently disillusioned with her hedge fund modelling that she joined the Occupy movement.

Grokking Algorithms Review and Author Q&A

Posted by Sergio De Simone on  Jul 14, 2016

Manning’s Grokking Algorithms takes a novel approach to introducing such complex matters as data structures, algorithms, and complexity by leveraging the power and expressiveness of illustration.

The InfoQ Podcast: Courtney Hemphill on VR, Augmented Reality, and the Importance of Animation in UX

Posted by Barry Burd on  Jul 01, 2016

Barry Bird talks to Courtney Hemphill. With over ten years of experience in software development, Hemphill has done full stack development for both startup and enterprise companies.

Beyond Data Mining

Posted by Tim Menzies on  Dec 06, 2013

In this article, author talks about the need for change in predictive modeling focus and compares four types of data mining:algorithm mining, landscape mining, decision mining and discussion mining.

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