Ashwini Chandrashekharaiah
|
Staff Data Scientist
Walmart Global Tech |
|
Ashwini is a Staff Data Scientist at Walmart Global Tech based out of Bangalore. She has close to 11 years of experience ranging from Java/Oracle Apps development for Supply Chain ERP to enterprise level machine learning products across assortment, pricing and customer domains.
Ashwini holds a Master of Management in Business Analytics from Indian Institute of Science, Bangalore. In Walmart, as part of Data Ventures Team, Ashwini is working on designing and developing monetizable Data Science solutions that provide insights that would enable efficient planning and strategic decisions. |
Bayesian Machine Learning & Sampling Methods--An Introduction
September 29, 10:15-11:00am PST
September 29, 10:15-11:00am PST
Prerequisites: Basics of Probability Distribution; Bayes Theorem, Theorem of Total Probability, Conditional Probability; Linear Regression Basics + Linear Algebra
At its root, Bayesian Machine Learning builds statistical models using Bayes’ Theorem. While these methodologies are less explored than their frequentist counterparts, BML finds widespread applications in domains where data is limited and explainability of a model is paramount (i.e., domains where deep learning fails!). In Walmart, BML is widely used in Healthcare and Shrink studies. With growing advancement in GPU availability and open-sourced sampling algorithms, BML is seeing traction like never before. However, Bayesian isn’t for the faint of heart! We are here to make it a tad simpler to start with.
In this workshop, you will learn about the core concepts of BML – how it is different from the frequentist approaches, building blocks of Bayesian inference and what known ML techniques look like in a bayesian set-up. You will also learn how to use various sampling techniques for bayesian inference and why we need such techniques in the first place. The workshop will also provide links and materials to continue your Bayesian journey afterwards.
This workshop is meant as an introduction to select BML modules - we strongly recommend you to continue exploring the world of bayesian once you have taken this first step. Happy learning!
At its root, Bayesian Machine Learning builds statistical models using Bayes’ Theorem. While these methodologies are less explored than their frequentist counterparts, BML finds widespread applications in domains where data is limited and explainability of a model is paramount (i.e., domains where deep learning fails!). In Walmart, BML is widely used in Healthcare and Shrink studies. With growing advancement in GPU availability and open-sourced sampling algorithms, BML is seeing traction like never before. However, Bayesian isn’t for the faint of heart! We are here to make it a tad simpler to start with.
In this workshop, you will learn about the core concepts of BML – how it is different from the frequentist approaches, building blocks of Bayesian inference and what known ML techniques look like in a bayesian set-up. You will also learn how to use various sampling techniques for bayesian inference and why we need such techniques in the first place. The workshop will also provide links and materials to continue your Bayesian journey afterwards.
This workshop is meant as an introduction to select BML modules - we strongly recommend you to continue exploring the world of bayesian once you have taken this first step. Happy learning!
Back to Workshop Instructors