Module 5: Machine Learning

Class ressources

• Hour 1 & 2:
  • [Supervised ML] Regression: slides
• Hour 3:
  • exercise on Airbnb data
• Hour 4
  • [Supervised ML] Classification: slides
• Hour 5:
  • exercise on predicting firms’ default
• Going further:
  • [Supervised ML] Trees and Forests: slides
  • [Unsupervised ML] : slides, notebook

Learning objectives

After this lesson, you should be able to:

• Understand the difference between
  • supervised and unsupervised learning
  • classification and regression
• Explain the following concepts (and why they are important):
  • training and test sets
  • overfitting
  • bias-variance tradeoff
  • cross-validation
  • hyperparameter tuning
• use the following ML algorithms:
  • linear regression
  • logistic regression
  • decision trees
  • random forests
  • k-nearest neighbors
• Use the scikit-learn library to train, select and use a model