ezequiel-aguilar-statistical-learning-stanford

About the course

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

Certificate (PDF)

Topics:

  • Ch1 Introduction
  • Ch2 Statistical Learning
  • Ch3 Linear Regression
  • Ch4 Classification
  • Ch5 Resampling
  • Ch6 Model Selection
  • Ch7 Nonlinear
  • Ch8 Trees
  • Ch9 SVM
  • Ch10 Unsupervised

 

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