1(a).6 - Outline of this Course - What Topics Will Follow?

In this course we will cover the following topics:

  • What is Statistical Learning (Supervised Learning and Unsupervised Learning)
  • Data Splitting, Model Building, and Cross-validation Techniques
  • Linear Regression and Variable Selection
  • Biased Regression, Shrinkage Methods of Regression
  • Dimension Reduction Techniques and Regression based on techniques thereof
  • Nonlinear Regression Techniques
  • Discriminant Analysis
  • Methods based on Decision Trees: CART, Bagging, Boosting, Random Forest
  • Support Vector Machines
  • Clustering Methods: Hierarchical Clustering, K-Means Clustering, kNN Method