Probabilistic Graphical Models (PGMs) for Fraud Detection and Risk Analysis.

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.

 
 

Outline/Structure of the Talk

The session shall talk about various applications followed by what is needed to acquire such skills and where to apply PGMs.

Learning Outcome

Attendees shall look at alternatives to deep learning.

Target Audience

Those in ML research, Fintech.

Prerequisites for Attendees

Python.

schedule Submitted 5 years ago

  • Dr. Dakshinamurthy V Kolluru
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  • Naoya Takahashi
    Naoya Takahashi
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  • Swapan Rajdev
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    Swapan Rajdev - Conversational Agents at Scale: Retrieval and Generative approaches

    Swapan Rajdev
    Swapan Rajdev
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    Conversational Agents (Chatbots) are machine learning programs that are designed to have conversation with a human to help them fulfill a particular task. In recent years people have been using chatbots to communicate with business, help get daily tasks done and many more.

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  • Mahesh Balaji
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    Mahesh Balaji - Deep Learning in Medical Image Diagnostics

    Mahesh Balaji
    Mahesh Balaji
    Innovation leader
    --
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    Talk
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  • Harish Kashyap
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    Harish Kashyap / Ria Aggarwal - Probabilistic Graphical Models, HMMs using PGMPY

    90 Mins
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    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.

  • Dr. Rohit M. Lotlikar
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    Dr. Rohit M. Lotlikar - The Impact of Behavioral Biases to Real-World Data Science Projects: Pitfalls and Guidance

    45 Mins
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    Anuj Gupta - Sarcasm Detection : Achilles Heel of sentiment analysis

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  • Dr. Arun Verma
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    Dr. Arun Verma - Extracting Embedded Alpha Factors From Alternative Data Using Statistical Arbitrage and Machine Learning

    45 Mins
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  • Tamaghna Basu
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    Tamaghna Basu - Killing the Password via Gesture Recognition

    Tamaghna Basu
    Tamaghna Basu
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    We are living in times where we still have to type the password. NeoEyed's gesture recognition is able to detect swipes to authenticate the user to provide access to the mobile. This is a non-trivial problem as many users can have similar gestures. It is important that the classifiers are able to detect fine changes between many users who could potentially break into the phone. We shall talk about how various classifiers are used for such a detection. We shall talk about classifiers such as one-class SVM, multi-level robust classifiers which are useful for this scenario. Our detection mechanism won us the "Paypal Award". We are now building the next generation authentication system for which we shall discuss the technologies and challenges that need to be resolved.

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    4. What directions are helping us. 4

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