Learning with Big Messy Data | Cornell University

Explore a wide range of machine learning and data analysis topics, including exploratory data analysis, linear regression, and feature engineering, with hands-on experience on real-world datasets.

Introduction

Slides, notes, demos, and videos for lectures will be posted here. Slides and demos will be updated immediately before lecture; later slides correspond to material from previous years. Topics may change based on student interest.

Highlights

• Covers a wide range of topics in machine learning and data analysis, including exploratory data analysis, linear regression, feature engineering, and more
• Provides hands-on experience with real-world datasets through demos and projects
• Taught by experienced faculty at Cornell University

Recommendation

This course is recommended for students interested in learning how to work with large, complex datasets and apply machine learning techniques to solve real-world problems. It is suitable for both undergraduate and graduate students in fields such as operations research, computer science, and statistics.