Bayesian networks (BNs) are graphical structures that capture the probabilistic relationships between several random variables. They are a natural fit for scenarios that can benefit from both causal as well as probabilistic semantics, thereby, gracefully combining prior knowledge (in causal form) and knowledge from data.
In this tutorial, we will discuss methods to construct Bayesian networks from prior knowledge and statistical methods to improve these models through data. We will cover the exact and approximate inference techniques as well as techniques to learn the parameters and structure of BNs. We will illustrate the BN modeling approach using a real-world case study.
Workshop course contents and schedule:
This workshop will cover:
- Introduction to Bayesian Networks.
- How Inference works in Bayesian Networks
- Parameter learning
- Bayesian structure learning
- Real-world applications
Full workshop schedule: https://hasgeek.com/anthillinside/bayesian-networks-tutorial/schedule
Participants should bring their own laptops to participate in the workshop.
About the instructor:
Ashish Kulkarni has over 10 years of industry experience and currently heads the Data Science Lab at Clustr. He holds a Ph.D. in Computer Science from IIT Bombay and has published papers in top tier conferences including AAAI, IJCAI and others. Prior to joining Clustr, he worked as an applied machine learning scientist at Amazon.
Date: Saturday, 18 January 2019
Time: 9:30 AM to 2:50 PM
Venue: Accel LaunchPad, 886/A, Confident Electra, Opposite to Koramangala Club, 17th E Main Road, Koramangala 6 Block, Koramangala, Bangalore - 560095
For tickets and other inquiries, email firstname.lastname@example.org or call 7676332020.