These are materials for an introduction to machine learning course Belize Central Bank, August 6, 2020. If you have any questions or comments please email me at hersh [at] chapman [dot] edu
- 9am – 10am
- 10:00am – 11am
- 11:00am – 11:15am
- Q & A
- 11:15am – 12pm
- Brief Introduction to R
- Classic statistical text on machine learning algorithms
- Excellent (free!) book on programming in R
- Garret Grolemund and Hadley Wickham, R for Data Science
- Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483-485.
- Chakraborty, C., & Joseph, A. (2017). Machine learning at central banks.
- Hersh, J., & Harding, M. (2018). Big Data in economics. IZA World of Labor, 451-451.
- Hersh, Jonathan; Engstrom, Ryan; Mann, Michael; Martin, Lucia;Mejía, Alejandra, (May, 2020). Mapping Income Poverty in Belize Using Satellite Features and Machine Learning
- Hellwig, Klaus-Peter. (2020). Predicting Fiscal Crises: A Machine Learning Approach. IMF Mimeo
- Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.
- To use R locally on your machine you must down load both of these
- To run R in the cloud create an account here