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For the purposes of this class, I think we can be a bit more targeted, especially in the papers that we read. We're going to list a few key "seed" papers. This list will grow as we discover more about your projects and the specific tasks you want to attack.

• Kaelbling et al. Reinforcement Learing: A Survey.
  The key starting point for those interesting in reinforcement learning. As noted in class, this is a class of ML algorithms that learn proper actions to take by receiving delayed rewards.

• Davison and Hirsh. Predicting Sequences of User Actions
  One in a series of papers about predicting the next UNIX command that a specific user will type.

• Bradley Rhodes' Remembrance Agent
  A continuously running automated information retrieval system.

• William Cohen. Learning Rules that Classify Email
  An early paper that uses RIPPER to learn user-readable rules. Also discusses TF-IDF, a popular word weighting scheme with some (post hoc) justification from information theory. RIPPER is apparently still available from William Cohen, though you may have to email him directly. Search for RIPPER on his homepage.