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学内で開催されるオンライン授業の情報漏えい防止のため,2020年9月16日以降は授業カタログの更新を見合わせています

Data Science for Practical Economic Research

Data Science for Practical Economic Research
This course is a companion-course of the course on Deep Learning offered by Dr. Michal Fabinger as well as the course on Applied Microeconomic Research offered by Prof. Andrew Griffen.

In this course we will study the fundamentals of Machine Learning. Topics include:
- Supervised machine learning: under-fitting and over-fitting, regularization, cross-validation, data augmentation.
- Unsupervised machine learning: clustering, factor analysis, principal component analysis, independent component analysis.
- Semi-supervised learning.

We will study the theoretical as well as practical aspects of these methods. While covering the methods, we will look at economics papers that are using these methods.

Students will be asked to find a "big" dataset (more than 20GB) and try all the methods we cover in class on this data set. This way, students will learn practical aspects of dealing with big datasets that cannot be loaded into RAM.

Students will be encouraged to use either R or 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
291324-02
GEC-EC6831L3
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
開講所属
経済学研究科
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 consists of four parts: (1) homeworks; (2) midterm; (3) final exam. Homeworks will address practical aspects of machine learning methods. Midterm and final exam will address theoretical aspects of machine learning methods.
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) - M. Mohri, A. Rostamizadeh and A. Talwalkar, "Foundations of Machine Learning" (The MIT Press, 2018, 2nd edition)
参考書
-
履修上の注意
Good training in mathematics will be helpful.
その他
First day of class: April 7, 2020