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T-107, Steinman Hall
140th St. & Convent Ave.,
New York, NY 10031, USA
June 28, 2017
Wed, June 28, 2017
1:00 PM – 5:00 PM EDT
NYU Kimmel Center, Classroom GC 475
60 Washington Square South
New York, NY 10012
"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 seminar, 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.
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.
Analytic Modeling: why do we build models, to explain or to predict?
The Bayesian network paradigm as a unifying framework.
The BayesiaLab software platform—artificial Intelligence in practice:
Expert knowledge modeling and reasoning under uncertainty.
Supervised & unsupervised machine learning for knowledge discovery in complex domains.
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.
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