Harish Kashyap
Machine Learning Scientist
MCG
location_on United States
Member since 5 years
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Harish Kashyap
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Harish Kashyap worked at various research labs and industries such as Nokia, Amazon Robotics and Schlumberger in Boston. He has a Masters from Northeastern University, Boston in Electrical Engineering. His Thesis was in the area of System Combination using Probabilistic Algorithms in Speech Recognition at BBN Technologies, Cambridge, MA. He worked at various research labs such as Mitsubishi Electric Research Labs (MERL), Schlumberger Doll Research and industries such as Nokia and MassMutual Financial Group. He has several publications and patents filed in the areas of ML. He has led various Data Analytics projects across US and Europe some of which have led to organizational savings of $8M+. He has built out the curriculum for Machine Learning training platform, refactored.ai for Colaberry, which is now part of SUNY Buffalo’s graduate ML course. Harish is an invited speaker at various events, companies and Universities including ODSC and represents personally at ODSC.
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Probabilistic Graphical Models (PGMs) for Fraud Detection and Risk Analysis.
45 Mins
Talk
Advanced
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.
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Probabilistic Graphical Models, HMMs using PGMPY
Harish KashyapMachine Learning ScientistMCG
Ria AggarwalMachine Learning EngineerMysuru Consulting Groupschedule 5 years ago
90 Mins
Workshop
Intermediate
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.
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