Graph Neural Networks (GNN) Courses and Certifications

Association for Computing Machinery (ACM)

Graph Mining Hamilton

Learn graph representation techniques for node classification, link prediction, and graph classification. Explore deep learning on graphs, GNNs, and their applications in drug repurposing and recommender systems.

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

Association for Computing Machinery (ACM)

DeepMem: Learning Graph Neural Network Models for Fast and Robust Memory Forensic Analysis

DeepMem: A graph-based deep learning approach for fast and robust memory forensic analysis, using novel memory graph models and neural networks to enhance object detection in raw memory dumps.

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

Strange Loop Conference

Dancing With Myself

Explore AI-generated choreography using motion capture and machine learning, blending technology with creative expression to reflect personal identity and movement style.

• YouTube
• 36 minutes
• Self-Paced
• Conference Talk

Applied Algebraic Topology Network

Bastian Rieck - Topological Graph Neural Networks

Explore topological data analysis in graph representation learning, focusing on novel techniques that incorporate topological features to enhance graph neural networks' expressivity and performance.

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

USENIX

Distributed Deep Graph Learning at Scale

Explores a novel system for scaling Graph Neural Network training to large real-world graphs, introducing pipelined push-pull parallelism for faster and more efficient distributed processing.

• YouTube
• 14 minutes
• Self-Paced
• Conference Talk

International Mathematical Union

The Mathematics of Artificial Intelligence

Explore the mathematical foundations of AI, its impact on problem-solving in mathematics, and the challenges in developing reliable AI systems. Gain insights into generalization, explainability, and AI applications in inverse problems and PDEs.

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

International Mathematical Union

Theory of Graph Neural Networks: Representation and Learning

Explore the theory behind Graph Neural Networks, covering approximation, learning properties, and connections to graph isomorphism, equivariant functions, and dynamic programming.

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

Institute for Pure & Applied Mathematics (IPAM)

Task Structure and Generalization in Graph Neural Networks

Explore graph neural networks' generalization properties, focusing on the interplay between task structure and architectural biases for improved learning and performance in algorithmic tasks.

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

Yannic Kilcher

NeurIPS 2023 Research Highlights - Circuits, Collaborative Filtering, Clustering, and Privacy

Overview of NeurIPS 2023 poster presentations covering graph neural networks, collaborative filtering, semi-supervised learning, and privacy assessment in image reconstruction, with insights on cutting-edge machine learning research.

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

Aleksa Gordić - The AI Epiphany

Non-Parametric Transformers - Paper Explained

Explore non-parametric transformers: innovative AI model for tabular data, relational reasoning, and causal mechanisms. Learn architecture, loss function, and connections to other neural networks.

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

GOTO Conferences

Graph Neural Networks - Algorithm & Applications

Explore graph neural networks, their architecture, and applications in AI. Learn about graph representations, deep learning algorithms, and real-world use cases in document classification and action recognition.

• YouTube
• 21 minutes
• Self-Paced
• Conference Talk

IEEE Signal Processing Society

Neural Diffusion PDEs, Differential Geometry, and Graph Neural Networks

Explore neural diffusion PDEs, differential geometry, and graph neural networks in this advanced webinar by Michael Bronstein, bridging theoretical concepts with practical applications in data science.

• YouTube
• 1 hour 15 minutes
• Self-Paced
• Free Video

Devoxx

Leveraging Graphs for AI

Explore graph theory's role in AI, from data structures to machine learning, with practical examples and applications in analytics, algorithms, and neural networks.

• YouTube
• 57 minutes
• Self-Paced
• Conference Talk

Institut des Hautes Etudes Scientifiques (IHES)

Reliable AI: From Applied Harmonic Analysis to Quantum Computing

Explore reliable AI through generalization in graph neural networks and explainability using applied harmonic analysis. Delve into limitations of digital hardware and connections to quantum computing.

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

Valence Labs

Machine Learning for Multi-Scale Molecular Simulation and Design

Explore multi-scale molecular simulation techniques using machine learning for accelerated MD simulations and MOF design, focusing on coarse-grained modeling and graph neural networks.

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