Introduction to Unsupervised Machine Learning

Introduction to Machine Learning: Unsupervised Learning explores how machines uncover structure, patterns, and relationships in data without labeled outcomes. In this course, you’ll learn how to analyze and visualize high-dimensional data using Principal Component Analysis, discover natural groupings through clustering methods like K-Means and hierarchical clustering, and tackle real-world challenges such as missing data and recommender systems. Through hands-on practice and thoughtful interpretation, you’ll build the intuition and practical skills needed to extract insight from complex, unlabeled datasets. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS), Master of Science in Artificial Intelligence (MS-AI), and Master of Science in Data Science (MS-DS) degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Artificial Intelligence: https://www.coursera.org/degrees/ms-artificial-intelligence-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder

• Explain goals and challenges of unsupervised learning
• Apply dimensionality reduction techniques
• Discover and interpret data structure with clustering methods
• Address missing data and recommender system issues

• Basic familiarity with topic terminology
• Willingness to engage in applied exercises or case work