Probabilistic Graphical Models using PGMPY
Probabilistic Graphical Models using PGMPY


This will be a hands-on workshop on Probabilistic Graphical Models using PGMPY library. Attendees shall learn about basics of PGMs with the open source library, pgmpy for which we are contributors. PGMs are generative models that are extremely useful to model various hierarchical and non-hierarchical models as well as stochastic processes. We shall talk about how fraud models, credit risk models can be built using Bayesian Networks. We shall also talk about Hidden Markov Models and showcase how thermostat control can be modeled. Generative models are also useful to measure causality and are great alternatives to deep neural networks, latter which, cannot solve such problems.. This workshop shall have students learn basics needed to learn about Bayesian Networks, Markov Models, HMMs including advanced probability and other math basics needed to understand the topic. Students shall learn by examples through this workshop.


Ria Aggarwal is an experienced Engineer with a demonstrated history of working in the wireless industry, now working in the field of Machine Learning/Artificial Intelligence. She completed her graduation from Indian Institute of Technology Roorkee. She has always been passionate about Maths and Algorithms, machine learning being a union of both was a natural succession for her. She is currently working in a AI based R&D startup ‘Voyagenius labs'. Her work involves architecting machine learning solutions for solving real life problems with primary focus on research in the fields of Natural Language Processing, Reinforcement Learning and Bayesian statistics. She truly believes that probabilistic approach can give super powers to many Machine learning algorithms and is going to revolutionise the paradigm of predictive modelling

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