10-601 CF

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This a lecture used in the Syllabus for Machine Learning 10-601



  • Nothing is assigned (this is not covered in Mitchell).


You should know:

  • What collaborative filtering is.
  • How nearest-neighbor methods for CF work.
  • How to formulate CF as a regression or classification problem.
  • How matrix factorization can be used for CF.
  • How PCA, SVD, k-means, and other clustering methods relate to matrix factorization.
  • Why computer scientists tend to get Halloween and Christmas confused.