Machine Learning for Economists

This course is specifically designed for economists with background in Econometrics. It assumes knowledge of mainstream statistical methods such as OLS and maximum likelihood estimation to shed light on their usage with more complex datasets requiring machine learning for feature extraction. By the end of the course, you will be able to apply machine learning algorithms to reduce data dimensions, create new set of features from the raw data as well as develop competitive ensemble models from lower-level algorithms.

Curriculum

  1. Machine learning for economics: use cases and overview
  2. Overview of Generalized Linear Models
  3. Introduction to Tree-based Modeling
  4. Model Evaluation: relative and absolute metrics
  5. Model Selection using Cross-Validation
  6. Variable Selection and Regularization
  7. Feature extraction using Natural Language Processing
  8. Dimensionality Reduction Techniques
  9. Ensembling techniques

Prerequisites

  • Probability theory and calculus
  • Experience with regression and discrete choice models
  • Experience with one of the following analytical packages: IBM SPSS, Stata, Eviews, Python/R

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