Machine Learning | Data-Centric Web Systems - Cornell University

Comprehensive understanding of modern machine learning techniques and their practical applications, including classification, structured models, and hands-on experience.

Introduction

This course will introduce you to technologies for building data-centric information systems on the World Wide Web, show the practical applications of such systems, and discuss their design and their social and policy context by examining cross-cutting issues such as citizen science, data journalism and open government. Course work involves lectures and readings as well as weekly homework assignments, and a semester-long project in which the students demonstrate their expertise in building data-centric Web information systems.

Highlights

• Covers a fundamental set of machine learning techniques and algorithms, ranging from classification methods like decision trees and support vector machines, to structured models like hidden Markov models, and clustering and matrix factorization methods for recommendation
• Discusses principles and approaches from a theoretical perspective, including statistical learning theory, online learning, and experimentation techniques
• Provides hands-on experience through weekly homework assignments and a semester-long project

Recommendation

This course is recommended for students interested in gaining a comprehensive understanding of modern machine learning techniques and their practical applications. It is suitable for those with a background in programming, linear algebra, and probability theory.