Formation BayesiaLab : Day Introductory Course in Paris – Artificial Intelligence

    Formation BayesiaLab : Day Introductory Course in Paris – Artificial Intelligence
    Actu IA
    3-Day Introductory Course in Paris _ Artificial Intelligence with Bayesian N
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    Date / Heure
    Date(s) - 18/09/2019
    9h00 - 17h00

    Emplacement
    Multiburo Paris Av. Montaigne

    Catégories


    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 as biostatistics, decision science, econometrics, ecology, marketing science, 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 1,000 researchers from all over the world have taken this course. For most of them, Bayesian networks and BayesiaLab have become crucial tools in their research projects.
    Course Overview
    Day 1: Theoretical Introduction

    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.
    What’s 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.

    Course Preview

    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.

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