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Exam 2 Study Guide – Legacy

Exam 2 will cover all material on the schedule since Exam 1. The exam is closed book, closed notes. No calculator is allowed. The topics and readings are as follows:

Topics

  • MC2 Lesson 6, Technical analysis
  • MC2 Lesson 7, Dealing with data
  • MC2 Lesson 8, The Efficient Markets Hypothesis
  • MC2 Lesson 9, The fundamental law
  • MC2 Lesson 10, Portfolio optimization and the efficient frontier
  • MC3 Lesson 5, Reinforcement Learning
  • MC3 Lesson 6, Q-Learning (Part 1)
  • MC3 Lesson 7, Q-Learning (Part 2) & Dyna
  • Options
  • Movie: The Big Short
  • ML methods for time series data
  • Technical trading

Readings

  • “What Hedge Funds really do”, Chapter 12: Overcoming data quirks to design trading strategies
  • “What Hedge Funds really do”, Chapter 8: The Efficient Market Hypothesis(EMH) – its three versions
  • “What Hedge Funds really do”, Chapter 9: The fundamental law of active portfolio management
  • “Machine Learning”, Chapter 13, Reinforcement Learning

Legacy

  • 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