United Arab Emirates University, Al-Ain, UAE
March 3–5, 2020, 9 a.m. to 5 p.m. (daily)
Go beyond descriptive analytics and enter the realm of probabilistic and causal reasoning with Bayesian networks. Learn all about designing and machine-learning Bayesian networks with BayesiaLab (see complete course program)
This highly acclaimed course gives you a comprehensive introduction that allows you to employ Bayesian networks for applied research across many fields, such a biostatistics, decision science, econometrics, ecology, marketing science, sensory research, sociology, just to name a few.
The hallmark of this three-day course is that every segment on theory is immediately followed by a corresponding practice session using BayesiaLab. Thus, you have the opportunity to implement on your computer what the instructor just presented in his lecture. This includes knowledge modeling, probabilistic reasoning, causal inference, machine learning, probabilistic structural equation models, plus many more examples.
To date, over 1,000 researchers from all over the world have taken this course (see testimonials). For most of them, Bayesian networks and BayesiaLab have become crucial tools in their research.
The traditional classroom setting remains the most popular option to take this course. The small group size ensures that you get plenty of opportunities to ask questions and get one-on-one coaching by the instructor.
If you are affiliated with a university, non-profit organization, or government agency, you may be eligible for special pricing as per the registration form below.
As an alternative to joining our classroom session in person, you can also participate via a simultaneous livestream from your home or office anywhere in the world.
Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has been working in the field of Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks. After co-founding Bayesia in 2001, he and his team have been working full-time on the development BayesiaLab, which has since emerged as the leading software package for knowledge discovery, data mining and knowledge modeling using Bayesian networks. BayesiaLab enjoys broad acceptance in academic communities as well as in business and industry.
Applied researchers, statisticians, data scientists, data miners, decision scientists, biologists, ecologists, environmental scientists, epidemiologists, predictive modelers, econometricians, economists, market researchers, knowledge managers, marketing scientists, operations researchers, social scientists, students and teachers in related fields.
For a general overview of this field of study, we suggest that you download a free copy of our book, Bayesian Networks & BayesiaLab. Although by no means mandatory, reading its first three chapters would be an excellent preparation for the course.
"I would absolutely recommend this course as a thorough and in-depth introduction to Bayesian Networks and the BayesiaLab package. The small class sizes also contributed to an enjoyable and engaging learning experience."—Brian Potter, Infotools (Introductory Course in Melbourne, November 2015).
"This is one of the best
"Overall, this training was outstanding. Lionel is a gifted teacher, and it helps that you are showcasing a
“A must-take course for anyone looking to leverage advanced Bayesian network techniques in virtually any domain.”—Alex Cosmas, Chief Scientist, Booz Allen Hamilton (Introductory Course in Los Angeles, June 2011).
“The BayesiaLab software is impressive in its sophistication and multi-faceted abilities as a decision support tool. I had been using it primarily as a modeling tool for deductive analysis. Taking this class opened my eyes to BayesiaLab's incredible data-mining abilities. If you are looking for something that will provide a totally new angle on business decision problems, this is it!”—Michael Ryall,
"This class can only be described as eye-opening, the tool as terrific. Some of the best instruction for the shortest period of time I’ve ever received. A seriously terrific job.” —Beau Martin, President of American Choice Modeling (Introductory Course in Chicago, July 2013).