Algorithms for Big Data | Harvard University CS 229R
Harvard University
Dive into the theoretical foundations of efficient algorithms for processing big data. Relevant for internet search, machine learning, and scientific computing.
University CoursesBig DataMachine Learning
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.
How GetVM Works
Learn by Doing from Your Browser Sidebar
Access from Browser Sidebar
Simply install the browser extension and click to launch GetVM directly from your sidebar.
Select Your Playground
Choose your OS, IDE, or app from our playground library and launch it instantly.
Learn and Practice Side-by-Side
Practice within the VM while following tutorials or videos side-by-side. Save your work with Pro for easy continuity.