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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.
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時間割/共通科目コード
コース名
教員
学期
時限
5171023
GPP-DP6E70L3
国際金融・開発研究:経済学系(Data Science for Practical Economic Research)
Kucheryavyy Konstantin
S1 S2
火曜4限
マイリストに追加
マイリストから削除
教室
講義使用言語
英語
単位
2
実務経験のある教員による授業科目
NO
他学部履修
不可
開講所属
公共政策学教育部
授業計画
Preliminary plan of lectures (subject to change): Lecture 1: Overview of Machine Learning methods; main concepts. Lecture 2: Linear regression Lecture 3: Classification methods Lecture 4: Resampling methods Lecture 5: Linear model selection and regularization Lecture 6: Nonlinear methods Lecture 7: Tree-based methods Lecture 8: Support vector machines Lecture 9: Boosting Lecture 10: Neural networks
授業の方法
Lectures. Demo programs.
成績評価方法
Grade will be based on homeworks.
教科書
The lectures will based on: - G. James, D. Witten, T. Hastie and R. Tibshirani, "An Introduction to Statistical Learning, with applications in R" (Springer, 2013) - T. Hastie, R. Tibshirani and J. Friedman, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" (Springer, 2009)
参考書
NA
履修上の注意
- Good command of Python is assumed. - Students are assumed to be familiar with basics of Machine Learning.
その他
First day of class: April 6, 2020