Exam 2 Study Guide

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This applies to online students only. The on campus test will be different.

Exam 2 will consist of approximately 3 multiple choice questions on each topic listed below:

  • Comparison of different regression learner performance characteristics: Trees, forests, KNN, linreg
  • Comparison of learner types: Regression, Classification, RL
  • Overfitting: Definition, how to identify, what might prevent it, what might cause it?
  • Bootstrap aggregating.
  • Boosting.
  • Decision trees. Random versus information based construction. Advantages of one over the other.
  • Reinforcement learning: How is it defined? Questions about State, Action, Transitions, Reward
  • Q-Learning. The update equation, definition of Q
  • Dyna-Q
  • Things you should know because you did the projects. In sample versus out of sample. Istanbul problem, why did shuffling help?
  • Options

Readings:

  • "Machine Learning", Chapter 1, Introduction
  • "Machine Learning", Chapter 8, Instance-based Learning
  • "Machine Learning", Chapter 3, Decision Tree Learning
  • "Machine Learning", Chapter 3, Decision Tree Learning
  • Paper: "Perfect Random Tree Ensembles" by Adele Cutler
  • "Machine Learning", Chapter 13, Reinforcement Learning

The exam will include 30 questions. You will have 30 minutes (plus 5 extra in case of a problem) to complete the exam. The exam is closed book, closed notes. You can use an on screen calculator.