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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