MESA 8414 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: Strong Python programming skills, including experience in data manipulation using pandas. Proficiency with statistical learning concepts like regression, classification, unsupervised learning, regularization is also required. Completion of General Linear Models course or equivalent is also strongly recommended.