Machine Learning for Data Science | Cornell University

Explore key machine learning concepts and algorithms for data science, including dimensionality reduction, clustering, and probabilistic modeling.

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).