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BayesiaLab Course in Sydney

3-Day Introductory BayesiaLab Course in Sydney:
Bayesian Networks for Applied Research and Analytics

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.

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, petrochemistry, sensory research, sociology, just to name a few.

The hallmark of this 3-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. Given the strictly limited class size, the instructor is always available to coach you one-on-one as you progress through the exercises.

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After the end of the course, you can continue your studies as you will have access to a full 60-day license of BayesiaLab 5.4 Professional. Additionally, two workbooks, plus numerous datasets and sample networks help you to experiment independently with Bayesian networks.

To date, over 600 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 all their research projects.

Date and Location

April 20-22, 2015, 9 to 5 pm each day.

Regus - Australia Square
95 Pitt Street
Sydney, NSW 2000

Course Overview

Bayesian NetworkPart 1: Theoretical Introduction

  • Introductory Exercises
  • Probability Theory
  • Bayesian Networks
  • Building Bayesian Networks Manually 
  • Machine-Learning Bayesian Networks
  • Bayesia Market Simulator
  • Miscellaneous Applications
Unsupervised Learning

Part 2: Machine Learning

  • Estimation of Parameters
  • Information Theory
  • Unsupervised Structural Learning
  • Supervised Learning
  • Semi-Supervised Learning - Variable Clustering
  • Data Clustering
  • Probabilistic Structural Equation Models


Please download the complete course syllabus for further details.

About the Instructor

Dr. Lionel Jouffe

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. 

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's required? 

  • Basic data manipulation skills, e.g. with Excel.
  • Working knowledge of specifying and estimating linear models.
  • Familiarity with factor analysis.
  • 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.

90-Minute Course Preview Video

Are you wondering what our introductory BayesiaLab course is like? Well, take a look! We've recorded the first hour and a half of the course that started on November 19, 2014, in Washington, D.C. In this segment, Dr. Lionel Jouffe provides an overview of the course program.

This is a good example of the way we typically host courses around the world. The groups are small, participants are from very diverse backgrounds; most importantly, the learning environment is always supportive and friendly.


Testimonials from Earlier Courses

“A must-take course for anyone looking to leverage advanced Bayesian network techniques in virtually any domain.” - Alex Cosmas, Chief Scientist, Booz Allen Hamilton

“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

"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

"Attend, attend, attend! The training was well done allowing for both hands-on using BayesiaLab but also exploration of the Bayesian approach. Lionel was a great teacher – to have the brain behind the product guiding you was indeed amazing, no question went unanswered." - Yianna Vovides, The George Washington University

Tems & Conditions

  • A 60-day license to the full version of BayesiaLab Professional Edition will be provided to all participants for installation on their computers prior to the event.
  • Participants will be required to bring their own WiFi-enabled computer/laptop to the seminar (Windows XP/Vista/7/8 or Mac OS X). Tablet computers, including the Microsoft Surface, are not suitable for the course.
  • The course fee includes all training materials.
  • Accommodation is at the participants' own expense.
  • *Proof of affiliation will be required upon registration.



Download Course Syllabus

Download the Course Syllabus (PDF, 21 pages, 1.5MB)