Bayesian Networks—Artificial Intelligence and
Virtual Reality for Research
Tuesday, November 14, 2017, 1:00 p.m. – 5:00 p.m.
NYU Kimmel Center, Classroom 912, 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 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.
With the recent launch of BayesiaLab 7, we can now leverage Virtual Reality methods to visualize Bayesian networks in three dimensions. This approach facilitates the exploration of large and complex networks, which were practically impossible to decipher in the past. Seminar participants will have the opportunity to try out BayesiaLab's VR module using the Oculus Rift.
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.
- 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
- High-dimensional network visualization and exploration using Virtual Reality
- Knowledge Encoding & Diagnosis
- Evidential Reasoning at Trial
- Reasoning About a Lost Bag
- Knowledge Discovery & Classification
- Breast Cancer Diagnostics
- Knowledge Discovery & Interpretation
- S&P 500
- Fundamental Stock Analysis
- ANSUR II Database
- Causal Inference
- House Price Analysis
- Establishing the Effect of Advertising
- Knowledge Encoding & Diagnosis
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 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.