Machine Learning | Cornell University CS4780/5780 Course

Dive into the fundamentals of machine learning with this comprehensive course from Cornell University. Explore supervised, unsupervised, and model selection techniques.

Introduction

This course provides an introduction to machine learning, covering fundamental concepts, algorithms, and applications. Topics include supervised learning (linear regression, logistic regression, support vector machines, neural networks), unsupervised learning (clustering, dimensionality reduction), and model selection and evaluation.

Highlights

• Covers a wide range of machine learning algorithms and techniques
• Includes both theoretical foundations and practical implementation
• Provides hands-on experience through programming assignments and projects
• Taught by experienced faculty from Cornell University

Recommendation

This course is recommended for students interested in machine learning, data science, and intelligent systems. It is suitable for both undergraduate and graduate students with a background in computer science, mathematics, and statistics.