国際金融・開発研究：経済学系（Data Science for Practical Economic Research）
Data Science for Practical Economic Research
This is an advanced (close to PhD level) class on fundamentals of Machine Learning. We will closely follow two textbooks:
- T. Hastie, R. Tibshirani and J. Friedman, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" (Springer, 2009)
- G. James, D. Witten, T. Hastie and R. Tibshirani, "An Introduction to Statistical Learning, with applications in R" (Springer, 2013)
During the lectures, we will cover chapters from these books and perform hands-on sessions. Also, we will cover some of the most recent academic papers on Machine Learning.
All homework assignments for this class will be practical: students will be asked to apply methods covered in the class to real datasets. The required programming language is Python.
Students taking this class will be assumed to be familiar with basics of Machine Learning as well as programming in Python.