Study entity and relation representations, schema-aware completion, efficient embeddings, and rigorous link-prediction evaluation.
This advanced course focuses on knowledge-graph completion rather than general graph neural networks. Learners connect structural and text features with entity–relation embeddings, examine efficient non-sampling objectives and schema-aware instance completion, and scrutinize evaluation protocols for link prediction. Across the sequence, attention stays on candidate construction, leakage, ranking metrics, and whether an evaluation reflects the intended use. The selected videos have English audio; original study materials are analytical guidance, not copied transcripts.
Represent knowledge-graph completion as prediction over typed entity–relation facts and distinguish it from generic node classification. Compare structural, text-augmented, and efficient embedding approaches while making their assumptions and computational trade-offs explicit. Audit a link-prediction evaluation for leakage, candidate-set bias, filtering choices, and alignment between metrics and the claimed task.
Prior familiarity with machine learning, vector embeddings, and classification or ranking metrics is recommended. Knowledge of graphs and basic linear algebra will help; prior knowledge-graph benchmark experience is not required.
Experienced learners who want to deepen and apply advanced AI skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Advanced · Full course.
This course uses CC BY licensed material attributed to its content provider or uploader. Lesson playback is available only through authenticated access after publication and licensing checks pass.