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Bayesian Networks: Artificial Intelligence for
Research, Analytics, and Reasoning

Monday, September 11, 2017, 1:00 p.m. – 5:00 p.m.
Bently Reserve, Farallon Room, 400 Sansome Street, San Francisco, CA 94111

Eventbrite - Free Seminar in San Francisco: Bayesian Networks—Artificial Intelligence for Research, Analytics, and Reasoning

"Currently, Bayesian Networks have become one of the most complete, self-sustained and coherent formalisms used for knowledge acquisition, representation and application through computer systems." (Bouhamed et al., 2015)

In this workshop, we illustrate how scientists in many fields of study—rather than only computer scientists—can employ Bayesian networks as a very practical form of Artificial Intelligence for exploring complex problems. We present the remarkably simple theory behind Bayesian networks and then demonstrate how to utilize them for research and analytics tasks with the BayesiaLab software platform. More specifically, we explain BayesiaLab's supervised and unsupervised machine learning algorithms for knowledge discovery in high-dimensional domains.

Also, while Artificial Intelligence is commonly associated with another buzzword, "Big Data," we show that Bayesian networks can bring Artificial Intelligence to problems for which we possess little or no data. Here, expert knowledge modeling is critical, and we describe how even a minimal amount of expertise can serve as a basis for robust reasoning under uncertainty with Bayesian networks.

  • Artificial Intelligence Seminar in Boston, June 2017
  • Artificial Intelligence Seminar in New York City, June 2017
  • Artificial Intelligence Seminar in Toronto, July 2017
  • Artificial Intelligence Seminar in Atlanta, July 2017
  • Artificial Intelligence Seminar in Bangalore, December 2016
  • Artificial Intelligence Workshop in Singapore, July 2015
  • Artificial Intelligence Workshop in Chicago, May 2015
  • Artificial Intelligence Workshop in Singapore, June 2015
  • Artificial Intelligence Workshop in New York City, January 2016
  • Artificial Intelligence Workshop in Singapore, June 2015
  • Artificial Intelligence Seminar in Washington, D.C., March 2017

The workshop's examples can also be found in Chapters 4, 6, and 7 in our book, Bayesian Networks & BayesiaLab: A Practical Introduction for Researchers, which can be downloaded free of charge.

Workshop Overview

  • Why Bayesian Networks?
    • What is Artificial Intelligence?
    • Why do we build models? To explain or to predict?
    • The Bayesian network paradigm as a unifying framework
    • How does this relate to Artificial Intelligence?
  • What is BayesiaLab?
    • The BayesiaLab software platform
    • Artificial Intelligence in practice:
      • Expert knowledge modeling and reasoning under uncertainty
      • Supervised & unsupervised machine learning for knowledge discovery 

Who should attend? 

Biostatisticians, clinical scientists, data scientists, decision scientists, demographers, ecologists, econometricians, economists, epidemiologists, knowledge managers, management scientists, market researchers, marketing scientists, operations research analysts, policy analysts, predictive modelers, research investigators, risk managers, social scientists, statisticians, plus students and teachers of related fields.

Please note that this seminar is geared towards applied researchers, NOT software developers or computer scientists. Questions related to algorithms, programming, scalability, architecture, infrastructure, etc., will be out of scope at this event. 

About the Presenter

Stefan ConradyStefan Conrady has over 15 years of experience in decision analysis, market research, and product strategy with Fortune 100 companies in North America, Europe, and Asia. Today, in his role as Managing Partner of Bayesia USA and Bayesia Singapore, he is recognized as a thought leader in applying Bayesian networks for research, analytics, and reasoning. In this context, Stefan has recently co-authored a book, Bayesian Networks & BayesiaLab - A Practical Introduction for Researchers.

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