Probabilistic Graphical Models, HMMs using PGMPY
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.
Outline/Structure of the Workshop
This workshop will begin by teaching the basics of advanced probability theories, stochastic processes along with various applications. This shall be followed by markov theory and HMMs.
1. Advanced Probability Theory
2. Stochastic Processes
3. Generative Models
4. Markov Theory
5. Hidden Markov Models
Students shall learn what are generative models, markov theory, PGMs and Hidden Markov Models. Students shall also learn to use pgmpy, which is an open source library for modeling HMMs. The learn by examples will help students to incorporate HMMs into their own work/research.
Those who are familiar with Machine Learning basics
Attendees are recommended to get their laptops and know basics of Python. We propose to use refactored.ai that has in-built jupyter. All you need is a browser sign up and start using it.
schedule Submitted 8 months ago
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The talk shall be organized as:
1. The problem we are trying to solve. 3
2. What classifiers help in such a scenario. 10
3. Challenges in existing out of box methods 3.
4. What directions are helping us. 4