Applied AI and Machine Learning

This class will provide a broad overview of various approaches to machine learning, including supervised and unsupervised learning. Students will learn about the fundamental algorithms used to train computers to learn. The course will also expose students to different application areas where data-driven decision-making is aided by machine learning (e.g., text classification, image recognition, and predictive modeling). Students will use the Python programming language and machine learning libraries (e.g., scikit-learn) to solve authentic problems. While working with authentic datasets, students will also learn about bias, accountability, and trust issues that arise when conducting human-centered data science. Required Background:Calculus I or equivalent, with a solid understanding of functions, graphing, limits, derivatives, and basic optimization in one variable.