View source for Machine Learning Algorithms for Trading

==Lesson 1: How Machine Learning is used at a hedge fund==
*introduce problem early
*Overview of use and backtesting
**Out of sample
**Roll forward cross validation
*Methods
**Linear regression
**KNN regression
**Decision trees Random Forest regression (considering to drop)
*Quiz: which algorithm makes most sense here?
*Supervised ML (intent is that the treatment here is light)
**Use: Regression
**Use: Classification
**Model type: Parametric
**Model type: Instance-based
*Quiz: What's the next point?
*Problems with regression for finance
**Hint at reinforcement learning
*Introduce the problem we will focus on in the rest of the class, namely:
**Example data, will learn on over a particular year (2012)
**Will test on over the next two years (2013 2014)
**It will be "easy" data that has obvious patterns
**You will create trades.txt and run them through your backtester

==Lesson 2: Regression==
[note: need to create fake stock data that has embedded patterns]
*Overview of how it fits into overall trading process
*Definition of the problem 1
**Black box diagram
**training: Xtrain, Ytrain
**using: Query with X
*Definition of the problem 2: APIs
**constructor
**addEvidence(X,Y)
**query(X)
*How to implement linear regression

==Lesson 3: Assessing a learning algorithm==
*Now that we have two, (linreg & KNN), let's compare them
**Pros and cons of LinReg versus KNN
***LinReg can extrapolate
***Kernel 
***Piecewise
**ease of adding new data
*Cross validation,
*roll forward cross validation
**Use all data versus most recent data
**Online learning
*How long to take to learn versus query
*Batch versus online
*RMS error
*Scatterplot predict vs actual
*Corrcoef
*Overfitting

==Lesson 4: Ensemble learners, bagging and boosting==

Mention that  this could mean different algorithms.

Bagging is an easy way to do this.

Boosting

perhaps include decision trees.

==Lesson 5: Reinforcement Learning==
*Classic view of the problem (from Kaelbling, Littman, Moore)
*Model-based
*Model-free

==Lesson 7: Q-Learning==

==Lesson 8: Dyna==