##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
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#Sponsors:
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
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Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more
AK
Ashish Kulkarni
@kulashish
Probabilistic Modeling – a tutorial on Bayesian Networks
Submitted Sep 7, 2019
Section:
Tutorials
Technical level:
Intermediate
Session type:
Tutorial
##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.
Outline
30 mins
Introduction to Bayesian Networks
- Recap of probability theory, random variables
- Conditional probability and independence
- Bayes theorem
- Joint probability distributions
- Markov condition
- Bayesian networks
45 mins
Inference in Bayesian Networks
- Message passing algorithm
- The Noisy-OR model
- Variable elimination
- Continuous variable inference
- Approximate inference techniques
45 mins
Parameter learning
- Learning Parameters in a Bayesian Network
- Learning with Missing Data Items
- Multinomial Variables
- Continuous Variables
45 mins
Bayesian structure learning
- Learning Structure: Discrete Variables
- Model Averaging
- Learning Structure with Missing Data
- Probabilistic Model Selection
- Learning Structure: Continuous Variables
45 mins
Applications
Speaker bio
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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Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more
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