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BayesiaLab

Free Workshop in Gardena, California:

Seminar_A_2Bayesian Networks: Artificial Intelligence for Research, Analytics, and Reasoning

Thursday, January 21, 2016, 10:00 a.m. - 12:00 p.m.

"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)

BayesiaLab

The objective of this workshop is to show that "Artificial Intelligence" should not be perceived as a quasi-magic technology that is mostly incomprehensible to normal mortals. We want to illustrate how scientists in any field of study—rather than only computer scientists—can employ A.I. to explore complex problems. For this purpose, we present Bayesian networks as the framework and BayesiaLab as the software platform. In this context, we demonstrate BayesiaLab's supervised and unsupervised machine learning algorithms for knowledge discovery in high-dimensional, unknown domains.

Also, while A.I. is commonly associated with another buzzword, "Big Data", we wish to prove that AI can be useful for dealing with 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 aided by A.I.

  • 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 Workshop in East Brunswick, NJ, January 2016

The workshop's examples can also be found in Chapters 4, 6, and 7 in our new 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 

Date and Location

Thursday, January 21, 2016, 10:00 a.m. - 12:00 p.m.
University of Phoenix
1515 W. 190th St.
Room 226
Gardena, CA 90248

Who should attend? 

Bioinformaticians, biostatisticians, clinical scientists, computer 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.

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 new book, Bayesian Networks & BayesiaLab - A Practical Introduction for Researchers.

Free Registration

Location & Map

University of Phoenix
1515 W. 190th St.
Room 226
Gardena, CA 90248

Please keep me posted about upcoming courses and events!