Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course

 

 

 

Statistical analysis and optimization of data

The following topics have been discussed:

  1. Basic concepts, expectation values, variance, covariance, correlation functions and errors;
  2. Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
  3. Central elements from linear algebra, matrix inversion and SVD
  4. Gradient methods for data optimization
  5. Estimation of errors using cross-validation, bootstrapping and jackknife methods;
  6. Practical optimization using Singular-value decomposition and least squares for parameterizing data.
  7. Principal Component Analysis to reduce the number of features.