STAT 406 – Statistical Learning Course Reflection

All I can say is props to Professor Barrera for not only conveying SO MANY concepts to us clearly, but also making it interesting with his weird sense of humor. This is one of the courses that made me think that EVER SINGLE concept taught in this class is going to somehow benefit me or be used in the future.

Topics covered:

  • Supervised and unsupervised learning
  • K-fold cross validation
  • Prediction models (linear, non-linear) and non-parametric models
  • Variable selection: step-wise, sequencing, shrinkage
  • LASSO, Ridge regression, Elastic net
  • Smoothers (local regression, kernal, splines)
  • Regression and classification trees
  • K-nearest neighbors, QDA, LDA
  • Logistic Regression
  • Bagging
  • Curse of Dimensionality
  • Boosting
  • Random Forests
  • Neural Networks

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