Join us for the third installment of the Machine Learning for Physical Science and Engineering Seminar Series. This is a great introduction for the true beginner looking for a place to start applying machine learning to your specific data.
"Machine Learning: A Beginner's Guide" presented by Dr. Matthew Argall, UNH
This seminar will present a high-level overview of machine learning (ML). It attempts to answer three big questions for beginners: What is it? What
models are out there? and How can I get started? To answer the first question, I start with a definition of ML and cover some early milestones that helped lead to the state of ML today. For the second question, I discuss the different branches of ML, including supervised and unsupervised learning, and present a flow chart that associates ML models with each branch of ML and provides a path between your dataset and applicable models. For the third question, I cover common tools in Python and R, then provide a conceptual overview of common ML models and pair them with examples from the literature in my field, space physics.
Please join us in DeMeritt Hall, Room 251, 1:00-2:00 pm.