Mon September 11, 2017
Free Seminar in San Francisco: Bayesian Networks—Artificial Intelligence for Research, Analytics, and Reasoning
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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 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.
Program Overview:
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?
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, computer scientists, database managers, or IT administrators. Questions related to algorithms, programming, scalability, data architecture, infrastructure, etc., will be out of scope at this event.
show less
"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.
Program Overview:
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?
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, computer scientists, database managers, or IT administrators. Questions related to algorithms, programming, scalability, data architecture, infrastructure, etc., will be out of scope at this event.
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 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.
Program Overview:
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?
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, computer scientists, database managers, or IT administrators. Questions related to algorithms, programming, scalability, data architecture, infrastructure, etc., will be out of scope at this event.
read more
"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.
Program Overview:
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?
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, computer scientists, database managers, or IT administrators. Questions related to algorithms, programming, scalability, data architecture, infrastructure, etc., will be out of scope at this event.
show less
Date/Times:
301 Battery Street, San Francisco, CA 94111
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