/Machine-Learning-in-Economics

An introductory course on machine learning in economics

Primary LanguageHTMLMIT LicenseMIT

Machine Learning in Economics

An introductory course on machine learning methods for economics majors.

Instructor: Prof. Dr. Hüseyin Taştan (Yıldız Technical University, Department of Economics)

Class Schedule

  1. Introduction:

  2. Basic Concepts in Machine Learning

  3. Introduction to the Tidyverse

  4. Linear Regression

  5. Classification

  6. Validation Methods and Bootstrap

  7. Model Selection and Regularization

  8. Nonlinear Models

  9. Regression and Classification Trees

  10. Unsupervised Learning

  11. Text Data

    • Lab (using tidytext package): html | .Rmd

    • Lab (using tm package) (by Alican Yıldırım): html | .Rmd

Scope and Purpose

The purpose of this course is to teach basic machine learning algorithms and methods relevant for empirical economists. The availability of large scale data sets has led to the development of new methods that are similar to those utilized in econometrics but also distinct in some aspects. This course, therefore, will emphasize the use of these new algorithms by focusing on statistical learning in economics and business. Students will learn the basic concepts, methods, and algorithms used in machine learning and develop skills to apply them in practice.

Textbook

James, Gareth, D. Witten, T. Hastie, R. Tibshirani (2021), An Introduction to Statistical Learning with Applications in R, 2nd ed., Springer.

Electronic version of the the book can be accessed:
https://www.statlearning.com/

Alpaydın, Ethem (2018), Yapay Öğrenme, 4. Baskı (Ethem Alpaydın, Introduction to Machine Learning, 2. baskıdan çeviri), Boğaziçi Üniversitesi Yayınevi, İstanbul

Software

We will use R in class and in lab sessions. R is an open-source software for statistical computing and graphics which is widely used by statisticians, researchers, data scientists and econometricians as well as industry professionals. The latest version of R can be downloaded from:

https://www.r-project.org/

And R-studio may be used as an integrated development environment for R:

https://www.rstudio.com/products/RStudio/

Datacamp for the classroom

This class is supported by https://www.datacamp.com/ through the "DataCamp for the Classroom" program.



Articles

Athey, S. (2018), ''The Impact of Machine Learning on Economics'', Stanford University, unpublished paper. https://projects.iq.harvard.edu/files/pegroup/files/athey2018.pdf, basılmış versiyon: (https://www.nber.org/chapters/c14009).

Athey, S. ve Imbens, G.W. (2019), ''Machine Learning Methods That Economists Should Know About'', Annual Review of Economics, 11: 685-725.

Kleinberg, J., Ludwig, J., Mullainathan, J., ve Obermeyer, Z. (2015), "Prediction Policy Problems", American Economic Review, Papers and Proceedings, 105(5): 491-495. http://dx.doi.org/10.1257/aer.p20151023

Mullainathan, S. ve Spiess, J. (2017) ''Machine Learning: An Applied Econometric Approach'', Journal of Economic Perspectives, 31(2), 87-106.

Samuel, A. L. (1959), ''Some studies in machine learning using the game of checkers'', IBM Journal, 3: 210–229.

Varian, H.R. (2014), ''Big Data: New Tricks for Econometrics'', Journal of Economic Perspectives, 28 (2): 3–28.

Videos

S. Mullainathan, Machine Learning and Prediction in Economics and Finance

S. Mullainathan, Machine Intelligence and Public Policy

Susan Athey, Machine Learning and Causal Inference for Policy Evaluation

T. Hastie, Statistical learning with big data. A talk by Trevor Hastie

Artificial Intelligence, the History and Future - with Chris Bishop

Youtube playlist: Introduction to Statistical Learning by Robert Tibshirani, Trevor Hastie, and Daniela Witten.

https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/