Schedule

Course content

Read an outline of topics and bibliography for this course

Course outline

1. Methodology: Introduction and Linear models
   Regression, Penalized Regression, LASSO, Ridge, Cross Validation, Least Squares.
2. Practice: Linear models
3. Methodology: Classification and introduction to flexible modeling
   Regression, Penalized Regression, LASSO, Ridge, Classification, AUC, ROC, Cross Validation, Least Squares,K-Nearest Neighbors, Decision Trees.
4. Practice: Classification and introduction to flexible models
5. Methodology: Supervised Learning with flexible models
   Nonlinear models, Random Forests, AdaBoost, Gradient Boosting.
6. Practice: Supervised Learning
7. Methodology: Unsupervised Learning
   Clustering, K-Means, Spectral Clustering, PCA, Factor Models.
8. Practice: Unsupervised Learning
9. Methodology: Data Science in R
10. Practice: Data Science in R

References

• The Elements of Statistical Learning. T. Hastie, R. Tibshirani, and J. Friedman. Springer Series in Statistics Springer New York Inc., New York, NY, USA, (2001)

• An Introduction to Statistical Learning: with Applications in R, G. James, D. Witten, T. Hastie, R. Tibshirani, Springer Series in Statistics Springer New York Inc., New York, NY, USA, (2021)
   

Prerequisites

Some knowledge of Python, R, Jupyter Notebooks, and matrix algebra is recommended to fully benefit from the course, which will be run in Google Collaboratory.

Those with limited or no experience in either R or Python must register separately for the course Introduction to Python and R Programming for Data Science, which will be conducted on March 5 and 6, 2024.

Students must have their own laptop or desktop computer and a good Internet connection to be able to follow and fully benefit from the course.