Graph Machine Learning Courses and Certifications

IEEE Signal Processing Society

Graph Constructions for Machine Learning Applications - New Insights and Algorithms

Explore graph constructions for machine learning, covering signal variation, sampling, and semisupervised learning. Gain insights into theoretical analysis and applications in deep learning geometry.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Scalable Parallel Computing Lab, SPCL @ ETH Zurich

Scalable Graph Machine Learning - Accelerating GNN Computations

Explore scalable graph machine learning techniques, focusing on Graph Neural Networks. Learn about efficient methods for training and inference, including graph sampling and partitioning for large-scale graphs.

• YouTube
• 1 hour
• Self-Paced
• Free Video

International Centre for Theoretical Sciences

Deepsets, Graph Neural Networks, and Transformers in Machine Learning - Lecture 4

Explore advanced deep learning architectures like Deepsets, Graph Neural Networks, and Transformers, focusing on their applications in high energy physics data analysis and research.

• YouTube
• 2 hours 3 minutes
• Self-Paced
• Free Video

International Centre for Theoretical Sciences

Deepsets, Graph Neural Networks, and Transformers in Machine Learning - Lecture 7

Explore advanced machine learning techniques for high energy physics, including Deepsets, Graph Neural Networks, and Transformers, to analyze complex particle physics data.

• YouTube
• 1 hour 26 minutes
• Self-Paced
• Free Video

MLOps World: Machine Learning in Production

Machine Learning on Dynamic Graphs - Temporal Graph Networks

Explore Temporal Graph Networks for dynamic graph analysis, unlocking insights in evolving systems like social networks and financial transactions.

• YouTube
• 45 minutes
• Self-Paced
• Free Video

Computational Genomics Summer Institute CGSI

Machine Learning via Graph Signal Processing for Complex Biological Data

Explore machine learning techniques using graph signal processing for analyzing complex biological data, focusing on geometric scattering networks and their applications in computational genomics.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Explainable Structured Machine Learning in Similarity, Graph and Transformer Models

Explores techniques for explaining complex machine learning models, focusing on similarity, graph, and transformer structures. Discusses applications in knowledge evolution, gender bias, and task-solving.

• YouTube
• 53 minutes
• Self-Paced
• Free Video

Databricks

Scaling Blockchain ML With Databricks - From Graph Analytics to Graph Machine Learning

Discover how Coinbase scales machine learning on blockchain data using Databricks, from analyzing billion-node transaction graphs to implementing Graph Neural Networks for fraud detection.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Harvard CMSA

Math and Machine Learning Program - Graph Theory and Combinatorics

Explore advanced concepts in graph theory and combinatorics through mathematical approaches to machine learning applications.

• YouTube
• 1 hour 13 minutes
• Self-Paced
• Free Video

International Centre for Theoretical Sciences

Deepsets, Graph Neural Networks, and Transformers in Machine Learning - Lecture 9

Explore advanced machine learning techniques for high energy physics: Deepsets, Graph Neural Networks, and Transformers. Gain insights into cutting-edge data analysis methods for particle physics research.

• YouTube
• 1 hour 26 minutes
• Self-Paced
• Free Video

International Centre for Theoretical Sciences

Deepsets, Graph Neural Networks, and Transformers in Machine Learning - Lecture 4

Explore advanced machine learning techniques for high energy physics, including Deepsets, Graph Neural Networks, and Transformers, with expert Sanmay Ganguly.

• YouTube
• 41 minutes
• Self-Paced
• Free Video

International Centre for Theoretical Sciences

Deepsets, Graph Neural Networks, and Transformers in Machine Learning - Lecture 2

Explore advanced deep learning architectures like Deepsets, Graph Neural Networks, and Transformers for high-energy physics applications in this comprehensive lecture.

• YouTube
• 1 hour 26 minutes
• Self-Paced
• Free Video

International Centre for Theoretical Sciences

Deepsets, Graph Neural Networks, and Transformers in Machine Learning - Lecture 1

Explore advanced machine learning techniques for high-energy physics, including Deepsets, Graph Neural Networks, and Transformers, with expert Sanmay Ganguly.

• YouTube
• 1 hour
• Self-Paced
• Free Video

Discover AI

Link Prediction with Graph Neural Networks Using DGL and PyG

Master link prediction in graph neural networks using DGL and PyG frameworks, implementing node connectivity prediction through practical code examples and negative sampling techniques.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Aleksa Gordić - The AI Epiphany

How to Get Started With Graph ML - Blog Walkthrough

Comprehensive introduction to Graph Machine Learning, covering applications, methods, and resources. Explores research challenges, GNN expressivity, and related subfields, providing a solid foundation for beginners.

• YouTube
• 47 minutes
• Self-Paced
• Free Video