Classical Machine Learning Notes and Basics — Statistical Arbitrage

Questions: Machine Learning Basics

Gradient Descent

  • What are Convex and Non-Convex Problems?
  • How does the relationship between the Rate Of Learning and Step Size change?
  • Gradient Descent vs Stochastic Gradient Descent?
  • What is Feature selection, transformation and extraction?
  • Learning vs Memoization?
  • What are the assumptions behind IID, stationarity and the same sample data?
  • Compare Train vs Validation vs Test Sets?
  • How to do Feature Representation?
  • What is Multi-Collinearity?
  • Following, Bias Vs Variance Trade-Off?
  • Lastly, Lasso vs Ridge Regression?
  • Bagging vs Boosting?
  • What are Boosting Base Models?
  • What is the Logit Function?
  • Better Data vs Better Model?

Machine Learning Essentials

  • Hessian Matrix vs Gradient Descent
  • Formula description
  • What is Derivation
  • Correlation vs Covariance
  • Covariance as graph
  • What are Eigen Values Intuition
  • What is a Value Vector
  • Eigen Value as Hinge
  • Laplace Transformation Intuition
  • Transformation as a tool
  • Lagrange Multiplier Intuition
  • Constrained Optimisation Problem

Data Science with R (Scheduling using Optrees)

  • DAG
  • Shortest Path Algorithm
  • Encoding the Problem as DAG
  • Solving using Optrees

Machine Learning Regression (Interpretation)

  • Null Hypothesis
  • Significance vs Non Significance
  • Intercept
  • Interpretation

Machine Learning/Deep Learning For Quant Finance



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