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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
• Custering Methods: Hierarchical Clustering, K-Means Clustering, kNN Method