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