Machine Learning for Data Science | Cornell University
Cornell University
Explore key machine learning concepts and algorithms for data science, including dimensionality reduction, clustering, and probabilistic modeling.
University CoursesData ScienceMachine Learning
Introduction
An introductory course in machine learning, with a focus on data modeling and related methods and learning algorithms for data sciences.
Highlights
Covers dimensionality reduction techniques such as PCA, SVD, CCA, ICA, compressed sensing, and random projection
Explores clustering methods including k-means, Gaussian mixture models, and the EM algorithm
Introduces probabilistic modeling topics like graphical models, latent-variable models, and inference
Covers regression if time permits
Recommendation
This course is recommended for students interested in machine learning and its applications in data science. It provides a solid foundation in key machine learning concepts and algorithms, making it a valuable addition to one's data science skillset. The course can be taken independently or in conjunction with CS4780/5780 (Machine Learning for Intelligent Systems).
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