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