AI Bootcamp VIII - Graphs in Machine Learning

This one week course will 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.

What to Expect:

• Daily Structure: Each day will feature one to two lectures, complemented by two or more practical, hands-on sessions. We will also have short talks either by participants or other people describing their experience with foundational models

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

How to Join the Bootcamp:

• Availability: Limited to 20 participants.
• Registration: Sign up using this link and complete the pre-screening Python Programming Quiz by 11:59 PM Pacific Time on November 28th. Please note that your enrollment will not be finalized until you have taken the quiz and received a confirmation email from the bootcamp organizers.
• Optional (but highly recommended): Email us about yourself and your research, and let us know how you think this bootcamp can support your work.
  
  Note: This bootcamp is open to Graduate Students, Post Docs, and Faculty

Contact:

● Bootcamp director: Reza Sadri

● Administrative assistant: Caroline Murphy

● TAs: Sahithi Ankireddy, Surya Narayanan Hari, Panteleimon Vafeidis, Manal Sultan, Gary Yang, Thierno Diallo, Alejandro Stefan Zavala, Jay Siri, Sibo Ma, Yuning Yu