3-Day Introductory BayesiaLab Course in Nashville, TN
September 25-27, 2016, 9 a.m. to 5 p.m. each day
Schermerhorn Symphony Center, One Symphony Place, Nashville, TN 37201
Note: This course is part of the pre-conference program of the 4th Annual BayesiaLab Conference.
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.
After the end of the course, you can continue your studies as you will have access to a full 60-day license of BayesiaLab 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 their research projects.
Day 1: Theoretical Introduction
- Examples of Probabilistic Reasoning
- Probability Theory
- Bayesian Networks
- Building Bayesian Networks Manually
Day 2: Machine Learning - Part 1
- Estimation of Parameters
- Information Theory
- Unsupervised Structural Learning
- Supervised Learning
Day 3: Machine Learning - Part 2
- Semi-Supervised Learning - Variable Clustering
- Data Clustering
- Probabilistic Structural Equation Models
Please see the BayesiaLab Library for a more detailed description of the course content.
About the Instructor
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.
- 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 that you download a free copy of our new book, Bayesian Networks & BayesiaLab. Although by no means mandatory, reading its first three chapters would be an excellent preparation for the course.
Finally, we've recorded the first 90 minutes of the introductory BayesiaLab course that we hosted recently in Washington, D.C. 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.