Nov 2019
18 Mon
19 Tue
20 Wed
21 Thu
22 Fri
23 Sat 08:30 AM – 05:30 PM IST
24 Sun
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Accepting submissions till 01 Nov 2019, 04:20 PM
##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to sales@hasgeek.com
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Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
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Hosted by
Ashish Kulkarni
@kulashish
Submitted Sep 7, 2019
##Details of the workshop (date, time, venue) and tickets available here: https://hasgeek.com/anthillinside/bayesian-networks-tutorial/
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 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 illustrate the BN modeling approach using real-world case study.
30 mins
Introduction to Bayesian Networks
45 mins
Inference in Bayesian Networks
45 mins
Parameter learning
45 mins
Bayesian structure learning
45 mins
Applications
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 lile AAAI, IJCAI and others. Prior to joining Clustr, he worked as an applied machine learning scientist at Amazon.
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