Difference between revisions of "Exam 2 Study Guide"
Jump to navigation
Jump to search
| (4 intermediate revisions by one other user not shown) | |||
| Line 1: | Line 1: | ||
| − | Exam 2 will cover all material on the schedule since Exam 1. | + | 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== | ==Legacy== | ||
| − | |||
* Comparison of different regression learner performance characteristics: Trees, forests, KNN, linreg | * Comparison of different regression learner performance characteristics: Trees, forests, KNN, linreg | ||
| Line 27: | Line 45: | ||
* Paper: "Perfect Random Tree Ensembles" by Adele Cutler | * Paper: "Perfect Random Tree Ensembles" by Adele Cutler | ||
* "Machine Learning", Chapter 13, Reinforcement Learning | * "Machine Learning", Chapter 13, Reinforcement Learning | ||
| − | |||
| − | |||
Latest revision as of 14:19, 6 February 2020
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
