Algorithms for Big Data | Harvard University CS 229R

Dive into the theoretical foundations of efficient algorithms for processing big data. Relevant for internet search, machine learning, and scientific computing.

Introduction

Big data is data so large that it does not fit in the main memory of a single machine, and the need to process big data by efficient algorithms arises in Internet search, network traffic monitoring, machine learning, scientific computing, signal processing, and several other areas. This course will cover mathematically rigorous models for developing such algorithms, as well as some provable limitations of algorithms operating in those models.

Highlights

• Covers mathematically rigorous models for developing efficient algorithms for big data processing
• Discusses provable limitations of algorithms operating in those models
• Relevant to a wide range of applications including Internet search, machine learning, and scientific computing

Recommendation

This course is recommended for students interested in the theoretical foundations of algorithms for big data processing. It provides a strong mathematical background and covers important limitations and tradeoffs in the design of such algorithms.