Lecture Notes

  1. Statistical Learning -Exploratory Data Analysis :Graphs and Descriptive Statistics with R

  2. Distributions - Density Functions :Probability Distributions in R

  3. Estimation- Hypothesis Testing : One-Sample t Test in R

  4. Linear Least Squares and Linear Regression

  5. Multiple Linear Regression- Interaction

  6. Classification: Logistic Regression, LDA,QDA,KNN

  7.  Midterm

  8. Resampling Methods: Cross-Validation

  9. Resampling Methods: The Bootstrap

  10. Linear Model Selection and Regularization :Ridge Regression

  11. Tree- Based Methods

  12. Support Vector Machines