Machine Learning for Economics and Finance

Applications with Python (& R)

Kursleitung

• Prof. Dr. Ole Wilms

  • Professur für VWL, insb. Makroökonomik und Fiskalpolitik

Kursübersicht:

• Introduction to Machine Learning

  • Introduction to Python (& R)

  • Python (& R) practice exercises

• 1. Supervised Learning: (Multi-)Linear Regressions

• 2. Supervised Learning: Classification Problems

• 3. Resampling Methods: Cross Validation

• Problem Set 1

• 4. Subset Selection & Shrinkage Methods:

  • Ridge Regressions

  • Lasso Regressions

• 5. Tree-Based Methods

  • Regression Trees

  • Classification Trees

  • Random Forest

• Problem Set 2

• 6. Deep Learning

  • Neural Networks

• Practice Exam

• Exam

Literatur:

• An Introduction to Statistical Learning - with Applications in Python (ISLP) by James, Witten, Hastie and Tibshirani.

• An Introduction to Statistical Learning - with Applications in R (ISLR) by James, Witten, Hastie and Tibshirani.

• Quelle: https://www.statlearning.com/