darentsai/ml-course-r

Statistical and Machine Learning

Topics

• Lab1: Model Assessment
  Evaluation, Cross-validation, and Bootstrap
• Lab2: Regularization
  Ridge, LASSO, and Elastic-net Regression
• Lab3: Classification I
  Logistic Regression and K-Nearest Neighbors
• Lab4: Classification II
  Naïve Bayes Classifier and Discriminant Analysis (LDA/QDA)
• Lab5: Support Vector Machines
  Support Vector Machine (SVM) and Support Vector Regression (SVR)
• Lab6: Decision Tree (CART)
  Classification and Regression Tree (CART)
• Lab7: Ensemble Learning
  Bagging and Boosting

References

1. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R. 2nd edition. Springer. 🔗
2. James, G, Witten, D, Hastie, T, Tibshirani, R and Taylor, J. (2023). An Introduction to Statistical Learning with Applications in Python. Springer. 🔗
3. Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edition. Springer. 🔗