Throughout Predicting Extreme Climate Behavior with Machine Learning, you'll explore both theoretical concepts and practical applications or machine learning and data analysis. You'll begin by analyzing unsupervised learning algorithms, mastering techniques like clustering and dimensionality reduction, and applying them to real-world climate datasets. You'll also explore supervised learning, gaining hands-on experience with algorithms such as Logistic Regression, Decision Trees, and Neural Networks. This course can be taken for academic credit as part of CU Boulder鈥檚 Master of Science in Data Science (MS-DS) degree offered on the 糖心vlog官网观看 platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder鈥檚 departments of Applied Mathematics, Computer Science, Information Science, and others. The degree offers 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 糖心vlog官网观看 are ideal for recent graduates or working professionals. Learn more: MS in Data Science: /degrees/master-of-science-data-science-boulder
Introduction to Unsupervised Learning and Techniques
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Skills You'll Learn
Supervised Learning, Climate Modeling, Machine Learning, Statistical Analysis, Unsupervised Learning
From the lesson
Unsupervised Learning: Dimensionality Reduction
Data can be viewed in higher and lower dimensions, and this module will help you explore this key aspect of data science. PCA/SVD are two key methods of unsupervised machine learning in terms of dimensional reduction
Taught By
Osita Onyejekwe
Assistant Professor