BayesiaLab 101 Course in Bangalore
Bangalore, April 1, 2019, 9 a.m. - April 1, 2019, 5 p.m.

Level 9 Raheja Towers 26-27 Mahatma Gandhi Road Bangalore, Karnataka 560 001

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TheBayesiaLab 101course is a new training program that helps you get started with Bayesian networks and the BayesiaLab 8 software platform. It provides you with an economical way to begin your journey into the world of research, analytics, and reasoning with Bayesian networks.

This innovative course program combines a60-day BayesiaLab Education Editionlicense with an extensive array oftraining materials(case studies, example networks, and data sets) for self-study, plus a dedicatedonline forumthat encourages discussions with your fellow course participants.

Additionally, course participants are invited to join a complimentaryone-day classroom sessionat the beginning of the course. Finally, at the end of the 60-day license period, you can complete an online quiz to receive yourBayesiaLab 101 Certificate.

The objective of BayesiaLab 101 is that you will become familiar with the "nuts and bolts" of Bayesian networks and BayesiaLab. That means you will understand what happens inside a Bayesian network, as opposed to just knowing how to operate a piece of software. This will enable you to recognize how Bayesian networks relate to your current analytics work, and how they might transform your existing analytics practice. and help further your career development. Please note, however, that the discussion of algorithms will be out of scope for this course.

Finally, BayesiaLab 101 is also an excellent preparationbut not a requirementfor the more comprehensivethat we offer on a regular basis around the world. For those who have hesitated to sign up for these professional three-day classroom-based courses, BayesiaLab 101 provides a shorter and gentler introduction but does not take any shortcuts.

Course Timeline

  • BayesiaLab 101Forum Launch: March 15, 2019
  • BayesiaLab Education License Period: March 25 through May 24, 2019
  • One-Day Classroom Session in Bangalore: Monday, April 1, 2019
  • Final Quiz (Administered Online on Demand): available May 1824, 2019

Classroom Session

The complimentary one-day classroom session at the beginning of the BayesiaLab 101 course focuses on the fundamentals of the Bayesian network paradigm, such as how Bayes's theorem facilitates probabilistic reasoning with graphical models. The hands-on exercises in the classroom session emphasize developing an intuition for Bayesian inference.Similarly, you will rehearse the basic functionality of BayesiaLab during the classroom session so you can you to work independently on exercises during the license period.

Another key objective of this introductory session is to give you a sense where Bayesian networks belong in the world of Data Science and Artificial Intelligence.Bayesian networksare not just another tool, but a fundamentally different framework that allows exploring questions, including causal questions, that would otherwise be impossible to answer.

It is important to note that the classroom session puts great emphasis on mastering the basics through elementary exercises before proceeding to more ambitious applications. We want you to develop an in-depth comprehension with regard to the underlying principles, so you can ultimately build complex models with confidence, such as the ones you will learn in one of the comprehensive three-day.

Classroom Session Agenda

  • The motivation for Bayesian Networks
    • The Promise, the Peril, and the Limitations of Artificial Intelligence
    • Human Cognitive Limitations & Biases in Reasoning
  • Background: A Conceptual Map of Analytic Modeling and Reasoning
    • X Inference Type: Probabilistic vs. Deterministic
    • Y Model Purpose: Observational vs. Causal Inference
    • Z Model Source: Data vs. Theory
  • Introducing Bayesian Networks as a Research Framework
    • Joint Probability Distributions
    • Conditioning & Marginalizing
    • Probability Calculus & Factorization
    • Bayes' Rule
    • Directed Acyclic Graphs
    • Introductory Examples for Probabilistic Reasoning
      • Where is my Bag?
    • Key Advantages of Bayesian Networks as a Modeling Framework
  • The BayesiaLab Software Platform
    • Introducing the User Interface
      • Graph Panel
      • Nodes and Arcs
      • Node Editor
      • Modeling and Validation Mode
      • Monitors and Evidence
    • Data Import
      • Variable Types
      • Discretization
    • Machine Learning with BayesiaLab
      • Supervised Learning
      • Unsupervised Learning
      • Learning = Searching
      • Minimum Description Length
      • Network Performance Evaluation

BayesiaLab Education Edition

During the BayesiaLab 101 course, you will use the Education Edition of BayesiaLab 8 on your own computer. The Education Edition is functionally equivalent to the full commercial edition of BayesiaLab Professional, with the following exceptions:

  • The number of variables/nodes is limited to 50.
  • The maximum size of a dataset for learning is 1,000 rows/records.

Requirements

  • Program participants must download and install BayesiaLab on a WiFi-enabled laptop/notebook with a Windows or Mac operating system prior to the start of the classroom session.
  • Technical support for the BayesiaLab installation is available during the week prior to the classroom session, but not on the day of the classroom session. All technical issues need to be resolved beforehand.
  • A Linux version of BayesiaLab is also available for download, but there will be no technical/installation support to those who choose to install it.
  • Participants must bring their computer to the classroom session. Using a mouse as a pointing device is highly recommended.

Terms & Conditions

  • Registrations for the BayesiaLab 101 course can be refunded up to 30 days prior to classroom session.
  • Within 30 days prior to the classroom session,cancellations will not be refunded butcan be applied to future training programs or BayesiaLab software products (prevailing list prices apply).
  • As of the date classroom session, refunds or credits will no longer be possible.
  • Bayesia reserves the right to cancel the course prior to the date of the classroom session for any reason.
  • In the event of the cancellation of the course by Bayesia, all feeswill be refunded to the participants in full within three days.
  • The BayesiaLab Education license is not renewable beyond the 60-day course period.
  • Bayesia is not responsible for any expenses the participants incur in the context of the course or in the event of its cancellation.
  • All travel expenses inconjunction with the course are the participant's own responsibility.
  • The BayesiaLabsoftware is licensed by Bayesia S.A.S.
  • All course fees are invoiced and charged by Bayesia Singapore Pte. Ltd.in Singapore Dollars

Event link: https://www.eventbrite.sg/e/bayesialab-101-course-in-bangalore-tickets-57281199610?aff=ebapi

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