Machine Learning
Machine learning is an important branch of applied mathematics that is increasingly used in medicine and biomedical research, artificial intelligence, and data analysis. In this course, students will be introduced to fundamental algorithms used in machine learning systems beginning with the method of least squares, or Linear Regression, move onto an introduction to electronics and Quantum Mechanics, and then practice these skills through case studies from research and industry. Student teams will choose a problem to study and use publicly available datasets to analyze the data using the methods learned in the course and identify opportunities for machine learning in health care, business, and engineering settings.
This class is open to 10th and 11th graders.
Instructors: Michael Kumaresan, Adjunct Faculty and Cooper Union Alumni, and Cooper Union Undergraduate Teaching Assistants
Prerequisites: none
Teaching method: Online, Real time, Synchronous. The instructor and teaching assistants will lead students through daily scheduled lectures, discussions and practice sessions.
Materials: A CU@Home kit will be provided to students living in the United States only.
Technology Requirements:
Class: Computer with camera and microphone to participate in online video class (Zoom) and program at the same time.
Project work: Computer with WiFi to use web-based software and file management system (Microsoft Office and Teams). Camera to collect images and video of your project and upload to presentation and portfolio.
Cost: $1850
Credits: 0.00
Course Code: STEM 216