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๐ŸŽ“Introductory BayesiaLab Course

Three-Day Introductory BayesiaLab Course in Miami Beach, Florida

Spaces โ€” 1111 Lincoln Road, Miami Beach, FL 33139 October 7โ€“9, 2024

Since 2009, our BayesiaLab courses and events have spanned the globe. From New York to Sydney, Paris to Singapore, we've touched down in cities worldwide (take a peek at our photo gallery!).

BayesiaLab Courses Around the World

We have a fantastic venue for our course. The classroom is in the heart of Miami Beach, just a few blocks from the glamorous South Beach.

The Introductory BayesiaLab Course is more than just a beginner's guide. It's a deep dive into applying Bayesian networks across diverse fields, from marketing science and econometrics to ecology and sociology. And we don't just stick to theory. Every conceptual lesson transitions seamlessly into hands-on practice with BayesiaLab, allowing you to apply what you've learned directly, whether in knowledge modeling, causal inference, machine learning, or more.

Over 2,000 researchers worldwide can vouch for its impact, many of whom have made Bayesian networks and BayesiaLab integral to their research. Don't just take our word for it - check out the testimonials!

Course Registration on Eventbrite

Course Program

Introduction

  • Bayesian Networks: Artificial Intelligence for Decision Support under Uncertainty

  • Probabilistic Expert Systems

  • Bayesian Networks and Cognitive Science

  • Unstructured and Structured Particles Describing the Domain

  • Expert-Based Modeling and/or Machine Learning

  • Predictive vs. Explanatory Models, i.e., Association vs. Causation

  • Examples:

Examples of Probabilistic Reasoning

Probability Theory

Bayesian Networks

  • Qualitative Part: Directed Acyclic Graph

  • Graph Terminology

  • Graphical Properties

  • D-Separation

  • Markov Blanket

  • Quantitative Part: Marginal and Conditional Probability Distributions

  • Exact and Approximate Inference in Bayesian networks

  • Example of Probabilistic Inference: Alarm System

Building Bayesian Networks Manually

Course Testimonials (2009-Present)

๐Ÿ‘pageCourse Testimonials

About the Instructor

  • Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. 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 worked in 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 of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.

FAQ

What is the course format?

The course is an instructor-led classroom-based program with a maximum of 15 participants. The small group size allows for one-on-one coaching during the hands-on exercises and facilitates a lively dialog between participants.

Who should attend?

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.

What background is required?
  • Basic data manipulation skills, e.g., with Excel.

  • No prior knowledge of Bayesian networks is required.

  • No programming skills are required. You will use the graphical user interface of BayesiaLab for all exercises.

  • For a general overview of this field of study, we suggest you read our free e-book, Bayesian Networks & BayesiaLab. Although not mandatory, reading its first three chapters would be an excellent preparation for the course.

Can I attend this course remotely?

No, this is an in-person course, and you will need to be present in the classroom.

What do I need to bring?

You must bring your own notebook or laptop computer running a 64-bit version of Windows or macOS. For macOS computers, BayesiaLab is compatible with both Intel and Apple silicon.

A tablet-type iOS or Android computer cannot run BayesiaLab and will not work for this course.

Before the course, you will receive download and activation instructions for BayesiaLab so that your setup is ready to go when the course starts.

A mouse as a pointing device is strongly recommended.

What is the cancellation and refund policy?

You may cancel your registration for a full refund of the course fees up to 30 days before the start of the course. If you cancel within 30 days of the event, your course fee will not be refunded. However, you will be able to apply 100% of the paid course fees towards future BayesiaLab courses

Can I park at the course venue in Miami Beach?

Yes, the 1111 Lincoln Road Building features a large parking garage. Please see the garage website for details: https://www.legacyparking.com/facilities/1111-lincoln-road-parking. Note that you can book your parking ahead of time and secure a discount.

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