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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.