CS224W: Machine Learning with Graphs | Stanford University

Explore state-of-the-art graph machine learning techniques, including graph neural networks, graph embedding, and graph algorithms, with hands-on experience on real-world datasets.

Introduction

This course provides an in-depth exploration of machine learning techniques for working with graph-structured data. It covers a wide range of topics, including graph neural networks, graph embedding, and graph algorithms, and their applications in various domains such as social networks, biology, and recommendation systems.

Highlights

• Comprehensive coverage of state-of-the-art graph machine learning techniques
• Hands-on experience with real-world graph datasets and projects
• Taught by leading experts in the field of graph machine learning
• Opportunity to collaborate with peers and contribute to the field

Recommendation

This course is highly recommended for students and professionals interested in machine learning, data science, and network analysis. It provides a solid foundation in graph-based machine learning and equips learners with the skills to tackle complex problems involving relational data.

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