COURSE UNIT TITLE

Offered By

Economics

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR FIRAT GÜNDEM

Offered to

Economics

Course Objective

This course will be an introduction to machine learning techniques and how to use them to help solve economic and social problems. This course is designed for economics and social science students who are interested in learning modern, scalable, computational data analysis methods (include machine learning, data science, big data, AI), and apply them to social and policy problems. This course will teach students:
1) What role Machine Learning can play in designing, implementing, evaluating, and improving Social Policy.
2) How Machine Learning methods work, how to use them, and how to building machine learning pipelines/systems.
3) How to tackle economic problems using Machine Learning methods and tools
This is a hands-on course where students will be expected to use RStudio to implement solutions to various policy problems. Prior experience with RStudio is better but not necessary. We will cover supervised and unsupervised learning algorithms and will learn how to use them for social problems in areas such as sustainability, economic development, and growth.

Learning Outcomes of the Course Unit

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Recomended or Required Reading

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer.

Taddy, M. (2019). Business data science: Combining machine learning and economics to optimize, automate, and accelerate business decisions. McGraw Hill Professional.

Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73.

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59.

Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685-725.

Yu-Wei, C. D. C. (2015). Machine learning with R cookbook. Packt Publishing Ltd.

Xie, Y., Dervieux, C., & Riederer, E. (2020). R markdown cookbook. Chapman and Hall/CRC.

Planned Learning Activities and Teaching Methods

There will be theoretical and applied parts of the class. Computer labs will be used in order to improve data skills via open-source programs such as R.

Assessment Methods

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

Will be determined during the semester.

Work Placement(s)

None

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes