Lecture 16 compresses knowledge across objects with predicates, quantifiers, and functions, then derives conclusions through substitution, unification, and definite-clause forward inference while exposing termination and completeness limits.
Lecture 17 defines a language model as a chain-rule factorization of sequence probability, compares n-gram and neural conditional models, and shows how sampling, temperature, and evaluation shape generation.
Lecture 18 classifies AI's social effects as benefits, misuse, accidents, and structural harms, then connects fairness audits, research ethics, copyright, and platform terms to accountable institutional choices.
Lecture 19 uses the Economics of AI deck to connect compute, data, distribution, and organizational complements to GDP, labor, and ideas-driven growth.
Lecture 20 is Percy Liang's fireside chat on career and research, CS221 and Stanford, and AI's future, with every attribution tied to the official video and editorial synthesis kept separate from auto-caption uncertainty.
A slide-grounded reconstruction of Fall 2025 Lecture 1, covering Course map and tools, A common language for graph data, Hand-designed features and representation learning while documenting the classroom material unavailable to self-learners.
A slide-grounded reconstruction of Fall 2025 Lecture 2, covering Encoder-decoder view, Similarity and the objective, Random walks while documenting the classroom material unavailable to self-learners.
A slide-grounded reconstruction of Fall 2025 Lecture 3, covering From fixed embeddings to deep encoders, The message-passing framework, Aggregation and update while documenting the classroom material unavailable to self-learners.
A slide-grounded reconstruction of Fall 2025 Lecture 4, covering The GNN design space, Message, aggregation, and update, GraphSAGE while documenting the classroom material unavailable to self-learners.
A slide-grounded reconstruction of Fall 2025 Lecture 5, covering Graph-data augmentation, Feature and structural augmentation, Supervision and loss while documenting the classroom material unavailable to self-learners.
A Fall 2025 slide-grounded reconstruction of Lecture 6, covering What distinguishability means, The Weisfeiler–Lehman test, An upper bound for message passing while documenting unavailable classroom material.
A Fall 2025 slide-grounded reconstruction of Lecture 7, covering The perfect-GNN thought experiment, Three levels of standard-GNN failure, Identity-aware encoding while documenting unavailable classroom material.
A Fall 2025 slide-grounded reconstruction of Lecture 8, covering Self-attention and message passing, The scope of graph attention, Positional and structural encodings while documenting unavailable classroom material.
A Fall 2025 slide-grounded reconstruction of Lecture 9, covering Heterogeneous graph schemas, Relation-specific messages, R-GCN while documenting unavailable classroom material.
A Fall 2025 slide-grounded reconstruction of Lecture 10, covering Knowledge graphs and completion, Triple scoring, TransE and relation patterns while documenting unavailable classroom material.
A Fall 2025 slide-grounded reconstruction of Lecture 11, covering Graph formulation of recommendation, The matrix-factorization baseline, Message passing in NGCF while documenting the public-material boundary.
A Fall 2025 slide-grounded reconstruction of Lecture 12, covering Limits of the tabular pipeline, Mapping relational databases to graphs, Temporal entity graphs while documenting the public-material boundary.
A Fall 2025 slide-grounded reconstruction of Lecture 13, covering The multi-relational bottleneck, RelGNN composite message passing, Relation-specific aggregation while documenting the public-material boundary.
A Fall 2025 slide-grounded reconstruction of Lecture 14, covering The goal of relational foundation models, Zero-shot relational transfer, PRODIGY's prompt graph while documenting the public-material boundary.
A Fall 2025 slide-grounded reconstruction of Lecture 15, covering Limits of transductive KG embeddings, Entity-inductive link prediction, The relation graph while documenting the public-material boundary.