Please come early, admission will be on a first come first served basis.
6:30 – 7: Networking and pizza
7 – 7:05: Intro to Capital One’s Center for Machine Learning
7:05 – 7:30: Explainable AI (description below)
7:30 – 7:55: Fuzzy Search Case Study (description below)
7:55 – 8:30: Networking
Explainable AI: Key Techniques, Academic Literature & Societal Implications by Melissa Louie, Principal Associate, Data Science, Capital One
This presentation will provide an overview of Capital One’s research into Explainable AI (XAI), an area of artificial intelligence focused on being able to produce more explainable deep learning models while maintaining a high degree of efficacy, with the broader goal of ensuring humans’ ability to understand, trust, and provide context around machine learning outputs. The presentation will begin with an overview of the two most commonly-used techniques in XAI, including LIME and Integrated Gradients, then touch on additional methods from the latest academic literature, and will end with an overview of the broader implications of XAI for society and for enterprises.
Case Study in Applying Fuzzy Search Models to Transaction Data by Keira Zhou and Sheenam Mittal, Data Engineers, Capital One
When you look at your credit card transaction statement, have you ever wondered what “SBUX” means, what store is it and where is it? In this talk, you’ll learn about what Fuzzy Search is, and hear an use case of how Capital One applies. The presentation will focus on how we pair Fuzzy Search with other Machine Learning algorithms to cleanse transaction statements and map back to specific merchants, such as extrapolating and mapping “Starbucks” from “SBUX.”
11 W 19th Street, 3rd Floor