Algorithms for Big Data | CMU 15 859 | David Woodruff

Explore advanced algorithms for big data analysis, including regression, subspace embeddings, and distributed computing. Taught by expert David Woodruff at Carnegie Mellon University.

Introduction

This course covers algorithms for big data, including topics such as least squares regression, subspace embeddings, matrix approximation, and distributed algorithms. The course is taught by David Woodruff at Carnegie Mellon University in the fall of 2020.

Highlights

• Covers a wide range of algorithms for big data problems
• Taught by an expert in the field, David Woodruff
• Includes both theoretical and practical aspects of big data algorithms
• Offers opportunities for scribing lectures and working on projects

Recommendation

This course is recommended for graduate students and advanced undergraduates interested in algorithms, data analysis, and big data. It provides a solid foundation in the theoretical and practical aspects of big data algorithms, and the project-based approach allows students to apply the concepts learned in class.