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Introductory BayesiaLab Course in Boston, Massachusetts

March 24–26, 2020

 

https://www.bayesia.com/hubfs/Boston2.mp4

Three-Day Introductory Course in Boston:
Artificial Intelligence with Bayesian Networks & BayesiaLab

CANCELLED

Regus — Financial District
225 Franklin Street, 26th Floor, Boston, MA 02110

March 24–26, 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.

Participation Options & Registration

Course Agenda

Course Details

Testimonials

"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 trainings I have ever had! Perfect topic that opens so many opportunities in any domain you could think of. Software is amazing and very intuitive. Presenter is extremely knowledgeable, patient and friendly."—Vladimir Agajanov, Moody's (Introductory Course in New York, January 2016).

"Overall, this training was outstanding. Lionel is a gifted teacher, and it helps that you are showcasing a first rate product. BayesiaLab is the most intuitive and easy-to-use machine-learning software available. It's a first-rate investment."—Felix Elwert, PhD, Vilas Associate Professor of Sociology, University of Wisconsin-Madison (Introductory Course in Chicago, May 2014).

“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, PhD, Professor of Strategy and Economics, Rotman Business School, University of Toronto (Introductory Course in Chicago, July 2013).

"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).

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