Predicting Extreme Climate Behavior with Machine Learning

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’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s 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 Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder

• 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
• Unsupervised Learning: Clustering
• In this module, we delve into the concept of clustering, a fundamental technique in data analysis and machine learning. Clustering involves grouping a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. This module will provide a comprehensive exploration of clustering, including its various derivations, such as hierarchical clustering and K-Means.
• Supervised Learning: Regressions
• Regression is a cornerstone technique in machine learning, particularly when working with continuous variables, and is essential for modeling relationships between variables and predicting outcomes. In this module, we will explore the fundamental principles of regression, focusing on linear regression.
• Supervised Learning: Logistic Regression, Decision Trees, and SVMs
• In this module, we will explore classification techniques, a critical aspect of supervised learning in machine learning. Classification is the process of assigning labels to input data based on its features, and it is widely used for tasks like spam detection, medical diagnosis, and image recognition. Throughout this module, we will explore several key classification methods, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM). Each of these techniques offers unique strengths and is suited to different types of data and problem contexts. By the end of this module, you will have a thorough understanding of how these classification algorithms work, how to implement them, and how to choose the right method for your specific supervised learning challenges.
• Supervised Learning: Neural Networks
• This final module dives into Neural Networks and its application to climate data, primarily with different activation functions, layers, neurons and architectural structures of the network.