Instructor: Thierry Denoeux

Description: This course is an introduction to statistical learning, with examples of application to econometrics.

Program

1. Introduction to machine learning
2. Model selection
3. Splines and generalized additive models
4. Tree-based and ensemble methods
5. Kernel-based classification and regression
6. Neural networks and deep learning

Recommended reading:

• An Introduction to Statistical Learning with Applications in R
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction
• Pattern Recognition and Machine Learning

Slides

1. Introduction
2. Model selection
3. Splines and generalized additive models
4. Tree-based and ensemble methods
5. Kernel-based classification and regression
6. Neural networks and deep learning

Exercises

1. K-NN regression and bias-variance decomposition
   1. Solutions
2. Model selection
   1. Solutions
3. Splines and generalized additive models
   1. Solutions
4. Tree-based and ensemble methods
   1. Solutions
5. Kernel-based classification and regression
   1. Solutions
6. Neural networks and deep learning
   1. Solutions
7. Final problem (August 21, 2024)

Exam

1. Final exam

Datasets

• Default credit card
• Movie buzz
• Expenditure and Default Data
• Countries
• Program effectiveness
• CP time series
• German health care
• Communities and crime (long version)
• Crime (short version)
• Bike sharing data