Graphs in Machine Learning

December 2–6, 2024
100 Powell-Booth

Objective:

To equip participants with the tools and knowledge to integrate graph-based machine learning techniques into their research or to explore these concepts further through classes or self-study.

Topics Covered:

• Introduction to Graphs and Graph Representation Learning
• Deep Dive into GNN Architectures
• GNN augmentation and training (practical usage)
• Link Prediction and Knowledge Graphs
• Scaling GNNs and Real-world Challenges

Prerequisites

• Python Programming
• Machine Learning Basics and experience with ML frameworks such as PyTorch or TensorFlow
• Graph Theory Basics
• Linear Algebra
• Multivariable Calculus
• Probability Theory

Registration:

Please take the pre-screening Quiz before 11:59 PM Pacific Time on Thursday Nov.  28th

Registration URL: https://caltech.instructure.com/enroll/LF86B9

Note: This bootcamp is open to Graduate Students, Post Docs, and Faculty

Contact:

Bootcamp director: Reza Sadri, [email protected]

Administrative assistant: Caroline Murphy

Deadline for Registration: November 28, 2024