When Machine Learning Fails
Abstract
Machine learning can be used to examine a variety of challenges. However, this brings in a number of risks that can sometimes be overlooked leading towards a disastrous effect. We’ll discuss those risks and what you, as data scientists, can do to help mitigate them.
Location
Virtual
Authors
Christopher Teixeira
(he/him)
Data Scientist
Christopher Teixeira is a Data Scientist with extensive experience applying statistics, applied probability,
and operations research to solve complex organizational challenges. Throughout his career, he has partnered with
diverse stakeholders to drive data-informed decision-making, helping organizations navigate the nuances of various
analytical techniques to find optimal solutions. Christopher has a proven track record of delivering code in multiple languages,
leading large-scale technical efforts, and responding to technical proposals and developing relationships with customers.
He holds an M.S. in Operations Research from George Mason University and a B.S. in Mathematics from Worcester Polytechnic Institute.