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.

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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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