"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.
- What is Artificial Intelligence?
- Why do we build models? To explain or to predict?
- Why Bayesian Networks?
- The Bayesian network paradigm as a unifying framework
- What is BayesiaLab?
Artificial Intelligence in practice:
Expert knowledge modeling and reasoning under uncertainty
Supervised & unsupervised machine learning for knowledge discovery
About the Speaker
Stefan Conrady has over 20 years of experience in decision analysis, market intelligence, 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.