Through the conference, we would have heard different speaker's perspective and experience with Data Science and AI. In this closing panel, we want to step back and look at any unanswered questions that the audience would have.

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.

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

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.