Techniques T14 reinterprets the Gaussian SVM as a linear vote over distance-based similarities, which gives the RBF network. Too many centers overfit, so k-means picks a few prototypes, and k-means itself is alternating optimization. T15 starts from the Netflix ratings data: one-hot encode user IDs, feed them into a linear network with the tanh removed, and you get matrix factorization R ≈ VᵀW, learned by alternating least squares or SGD. The lecture closes with a map of extraction models: boosting, neural nets, RBF networks, matrix factorization, and k-NN. These two lectures exist only as MOOC material. Neither the Fall 2024 nor the Fall 2026 schedule covers them, and no public homework problem does either.
In a climbing RAG system, 'recommend the next route' (progression) and 'recommend a similar route' (similarity) were conflated by a single hasSimilarRouteIntent() function, causing recommendation quality to collapse. The fix is a two-stage intent classification with a Regex Fast Path + LLM Fallback.
The RAG system's extractRouteReference() used a for...return pattern that grabbed only the first match — so when a user provided five completed routes, only one was used. The fix evolves through three layers: rule-based multi-entity extraction, user profile aggregation, and embedding centroid.
Query: 'I just sent Beauty in the Mirror 5.11b — recommend routes of similar difficulty.' The results came back full of routes with similar-sounding names, not similar grades. Root cause: dense embeddings compress multiple attributes into a single vector, and the rarity of the route name drowns out the grade signal. The fix: three layers of defense — metadata pre-filtering, query rewriting, and score fusion.