Probabilistic Graphical Models, HMMs using PGMPY by Harish Kashyap K and Ria Aggarwal at

PGMs are generative models that are extremely useful to model stochastic processes. I shall talk about how fraud models, credit risk models can be built using Bayesian Networks. Generative models are great alternatives to deep neural networks, which cannot solve such problems. This talk focuses on Bayesian Networks, Markov Models, HMMs and their applications. Many areas of ML need to explain causality. PGMs offer nice features that enable causality explanations. This will be a hands-on workshop where attendees shall learn about basics of graphical models along with HMMs with the open source library, pgmpy for which we are contributors. HMMs are generative models that are extremely useful to model stochastic processes. This is an advanced area of ML that is helpful to most researchers and ML community who are looking for solutions in state-space problems. This workshop shall have students learn basics needed to learn about HMMs including advanced probability, generative models, markov theory and HMMs. Students shall build various interesting models using pgmpy.
Details: confengine.com...
Conference: india.odsc.com/