ECO447 Machine Learning for Economists

Aims and Objectives

This course aims to introduce machine learning (ML) techniques to Economics students. In recent years these techniques have been applied to various problems in many fields, including Economics and Finance successfully. It is an active area for causal inference in Economics.

At the end of the course, the students shall acquire basic knowledge about widely used machine learning techniques. Course is an applied course, hence, students will be able to apply these techniques and compare them with econometric method. Students will become familiar with the pros and cons of applying these techniques. The main software used in this course for statistical programming is R. Students shall be able to use R and its related packages. The content of the course is dynamic and changing in every year.

The content of the course includes (but not limited):

• Introduction
  • Why?
  • Learning from Data
  • Econometrics and Machine Learning
    
    
• R
  • R and RStudio Installation & Resources
    
  • R Basics
  • Data Import
  • Rmarkdown for HW Assignments
    
    
• Introduction - 2 Data - Model - Analysis and Causality pdf
  • Data Types
    
  • Where to Start (Exploratory Data Analysis)
    
  • Summaries
    
  • Visualization
    
  • Causality
    
  • R examples pdf
    
    
• Regression
  • Statistical Learning: an Intro pdf
    
  • Regression Review pdf
    
  • Regression with Nominal/Ordinal Values pdf
  • Spline Regression pdf
    
    
• Cross Validation pdf
  
  
• Regression - 2 Regularization pdf
  • Polynomial Regression
    
  • Ridge Regression
    
  • LASSO (Least Absolute Shrinkage and Selection Operator) Regression
    
  • Elastic Net Regression
    
• Decision Trees, Bagging, Random Forest, Boosting pdf
  • Classification Trees
  • Regression Trees
  • Model Based Trees - SKIPPED
    
  • Random Forests
    
  • Model Forests - SKIPPED
    
  • Extreme Boosting
    
  • Causal Forests (Not Included)
    
    
• Similarity and Clustering - SKIPPED
  
  

References and Suggested Readings

• An Introduction to Statistical Learning, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, 2nd Ed. Click Here to Download Free Legal pdf Copy

• The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, Jerome Friedman, 12 printing. Click Here to Download Free Legal pdf Copy

• Practical Data Science with R, N. Zumel and J. Mount, 2nd Ed.

• Articles (to be distributed), Lecture Notes (from other universities)