Measure Theory and Machine Learning in Remote Sensing Data
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- place Craig-Lee 220
Join us for a talk on the use of Machine Learning to remote sensing as part of the 2024 Mathematical Sciences Lecture Series.
This lecture by Dr. Patricia Medina, CUNY, New York City College of Technology will explore how math theory connects with real-world data using dyadic sets and special measures.
We will introduce the dyadic product formula - a unique way to represent and compare these measures - and demonstrate its effectiveness with LiDAR data, a technology that maps the world in 3D by measuring distances, to distinguish trees from the ground. We'll also discuss previous research that used a different method for accurate data classification and how feature engineering might improve a machine learning model's accuracy.
The talk will be of particular interest to students in Mathematics, Data Science, Computer Science, and Artificial Intelligence.
This lecture is sponsored by the College Lectures Committee, Faculty of Arts and Sciences, and Department of Mathematical Sciences.
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