As part of a series of international collaborative courses offered by the Social Sciences and Humanities Unit for the Kyoto University Top Global Course (AGST), the Division of Natural Resource Economics will hold an intensive lecture course entitled “Introduction to Machine Learning for Agricultural and Food Economics” (Special Lecture on Natural Resources Economics IVA) , taught by Prof. Xiaohua Yu from the University of Göttingen, Germany on July 24, 26 and 27, 2023.
[Course Title]
[Intensive, First semester]
Special Lecture on Natural Resources Economics IVA （生物資源経済学特別講義IVA）(Code: FC05000)
“Introduction to Machine Learning for Agricultural and Food Economics”
[Number of credits]
1 (one)
[Instructor]
Prof. Xiaohua Yu
Chair Professor of Agricultural Economics in Developing and Transition
Countries, Courant Research Centre “Poverty, Equity, and Growth”, and
Department of Agricultural Economics and Rural Development,
University of Göttingen.
He is also Project Professor in the Top Global University Program at Kyoto University (2016-present).
https://www.reseco.kais.kyoto-u.ac.jp/en/jgp/profile-of-project-professors/
[Schedule]
July 24 (Mon.): 10:30-12:00 (Lect.1), 13:15- 14:45 (Lect. 2) & 15:00-16:30 (Lect. 3)
July 26 (Wed.): 08:45-10:15 (Lect. 4), 10:30-12:00 (Lect. 5) & 13:15-14:45 (Lect. 6)
July 27 (Thu.): 13:15- 14:45 (Lect. 7) & 15:00-16:30 (Lect. 8)
[Course Format]
In person at Room E217 of the Faculty/ Graduate School of Agriculture Main Bldg.
Click here for a map of the seminar room.
[Language]
English
[Overview and purpose of the course]
Machine learning is changing the world from different dimensions, and agricultural and food economics is no exception. In contrast to econometrics of causal analysis, machine learning put more emphasis on prediction and pattern recognition. This course will briefly introduce machine learning algorithms for research of agricultural and food economics. It will help students to master bask techniques in programing R (or Python) for machine learning.
[Syllabus & Flyer]
Please see Syllabus and Flyer for further information.
[Registration]
[For Academic Credit]:
Registration on KULASIS is already closed.
[For Auditing (Non-credit Participation)]:
Please register with Assistant Teaching Staff Mr. Makoto Kuroda by contacting him at: kuroda.makoto.6a[at]kyoto-u.ac.jp (please replace [at] with @)
by July 14, 2023.
[Contact]
Makoto KURODA, Assistant Teaching Staff, Division of Natural Resource Economics, Graduate School of Agriculture
E-mail: kuroda.makoto.6a[at]kyoto-u.ac.jp (please replace [at] with @)
Extension: 6187
