Debanjana Banerjee
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Senior Data Scientist
Walmart Global Tech |
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Debanjana is a Senior Data Scientist at Walmart Global Tech, India. At Walmart, she has been instrumental in building numerous ML-driven solutions in the compliance space dealing heavily in Natural Language Processing, Optimization, Mixture Models and Rare Time Series. Currently, her focus is on Site Content Recommendation for Walmart.com which utilizes NLP & Computer Vision for automated shelf curation. During her 4 years of experience, Debanjana has filed several US patents in the field of Clustering & Outlier Detection, Imbalance Text Classification, Travel Optimization and Stochastic Processes. In addition, she has three published papers to her credit. Most recently, her work in Anomaly Detection for Rare Time Series was included in ODSC Europe and APAC. Debanjana has a master's degree in Statistics from Indian Institute of Technology (Kanpur).
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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!
Workshop: Evolution of Applied Recommender Systems
Prerequisite: Basics of Linear Algebra and Linear Algebra for Regression; Concept of Features, Response & Parameters; Basics of Clustering and Neural Networks.
Machine learning driven Recommender Systems are undeniably one of the most crucial applications in modern technology. In this age of information, we are all in business with matching people to products, services, interests, information – you name it. Today, we depend on search engines and websites to show us what we like even before we know it! But the state-of-the-art Recommender Systems we know today are a result of consistent research taking shape for over three decades. In this workshop, we take you through the whirlwind journey of the recommender system from GroupLens in the 1990s, Content Based Filtering, Matrix Factorization and Hybrid Recommender Systems in the late 2000s all the way to DeepLearning based recommenders of today.
Machine learning driven Recommender Systems are undeniably one of the most crucial applications in modern technology. In this age of information, we are all in business with matching people to products, services, interests, information – you name it. Today, we depend on search engines and websites to show us what we like even before we know it! But the state-of-the-art Recommender Systems we know today are a result of consistent research taking shape for over three decades. In this workshop, we take you through the whirlwind journey of the recommender system from GroupLens in the 1990s, Content Based Filtering, Matrix Factorization and Hybrid Recommender Systems in the late 2000s all the way to DeepLearning based recommenders of today.
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