Nicole Maeser

Nicole is a second-year PhD student in the Bioinformatics and Computational Biology program at the University of Minnesota, developing and deploying artificial intelligence-based solutions to understand and treat cancer in the context of genomics. Along her academic and professional journey, Nicole has been teaching since 2013, and recently held a 4-hour workshop in May 2021 entitled "Introduction to Deep Learning and Creating Neural Networks in Python and R." She hopes to inspire a growth of technical skill and confidence in the field of artificial intelligence.

Nicole Maeser Course 2

Grades 1-6
TBD
60 minutes
Single Session
2-5 students group class

Mathematical Fundamentals of Machine Learning

Grades 6-12
The technology company IBM, founded in the early 20th century, cites more than 150 zettabytes of data will require analysis by 2025. With the abundance of data, artificial intelligence (AI) like machine learning (ML) is essential to solving some of the most challenging problems. In this course, students will gain exposure to the core mathematics behind basic ML-powered solutions in data analysis and become empowered in the critical field of AI. The curriculum includes an introduction to Bayesian decision theory, parametric estimation (e.g. maximum likelihood estimation) and dimensionality reduction, coupling theoretical lessons with hands-on exercises to enforce learning. We look forward to meeting you!
50 minutes per session
4 Sessions
$70 (4 Sessions)
2-5 students group class
Required experience with Cartesian coordinate system, functions (e.g. exponential, logarithmic) and their graphs, and transformations of functions.