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