IDI Bayesian Network Course will be November 13-15, 2018

IDI Bayesian Network Course will be November 13-15, 2018 2018-09-19T16:50:35+00:00
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Location: 8230 Old Courthouse Road, Suite 460, Vienna, VA 22182
Time: 8:00 am to 4:00 pm

Course Description: 

The course focus is on constructing and analyzing Bayesian networks (BNs) in Netica – from simple models for learning the basics to more complex, real-world applications for learning advanced features. Through a hands-on learning process, students will have the opportunity to explore examples and work on networks from their own work places during class exercises.

The cost is $1950 which includes snacks each day. A discount of $200 for early registration by July 14th is available. We also offer a $200 discount for multiple persons from the same organization. Only one discount can be used.

A temporary student license for the Netica software will be provided. Students do not need to purchase Netica for the course. PLEASE bring your own laptop. The course is very hands-on. Click here for this info in a pdf format.

Instructors:

Further Information:

Please keep an eye on this webpage for further information and updates. We can also teach this course at your site at another time. Email Dennis at dmbuede@innovativedecisions.com with questions or suggestions.

Course Topics (order is subject to change):

Day 1

  • Introduction
  • Building a simple Bayesian network (BN) – drug testing
  • Just enough probability
  • A complete diagnostic BN – Liver diagnosis case study
  • Elicitation of BN structure and probabilities
  • Causal Models

Day 2

  • Troubleshooting systems – Netica car diagnosis example
  • Troubleshooting systems – Industrial system example
  • Learning probabilities for a BN from data
  • Student Workshop (opportunity to work on your own BN problem)
  • D-Separation and sensitivity to findings
  • Combining expert knowledge and data
  • BN Building Practicum
  • Learning structure for a BN from data

Day 3

  • Netica API
  • Student Workshop
  • Learning a BN with continuous variables
  • Introduction to GeoNetica™
  • Student Workshop
  • Dynamic Bayesian networks / Incorporation of evidence over time

Advanced topics available on a case-by-case basis

  • Continuous variables – Model aggregation example & discretization
  • Named probability distributions in Netica
  • Inference and basics of the propagation algorithm behind BNs
  • Bayesian network formulation practicum – build lots of BNs
  • Decision making in Netica using influence diagrams
  • Intro to integrating Netica with Excel
  • Using the Netica library in the ExtendSim discrete event simulation
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Register Today!