Sinduja Subramaniam
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Staff Data Scientist
Walmart |
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Sinduja is a Staff Data Scientist/Manager at Walmart Global Tech in California with 6+ years of experience in tackling personalization challenges and problems. At Walmart Global Tech, Sinduja is both an independent contributor and manager of a team of data scientists. She leads data and relevance initiatives around customers' repurchase journey, page-level model design, particularly on the home page. Sinduja has a Master's degree in Computer Science, specializing in big data and machine learning, from the University of Illinois at Urbana Champaign. She also holds multiple patents..
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Workshop: An Introduction to Time Series Forecasting
October 27, 9:30-10:15am PST
October 27, 9:30-10:15am PST
Forecasting using time series data is a hot topic of research and is applied to a variety of use-cases to make important decisions - wherever there are changes with time (seasonal or trend) such as e-commerce orders, stock market prices, weather prediction, demand and usage of products, etc. This workshop will cover time series analysis that attempts to understand the nature of the series and is useful for future forecasting along with the overview of popular forecasting models such as ARIMA, SMA, SES, Prophet followed by a case-study walk-through.
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.