P000171, Introduktion till maskininlärning inom jordbruksekonomi, 3.5 Hp
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Kursplan

Fastställd av: Jens Rommel, 2026-02-03
Giltig från och med : Första halvår 2026 (2026-01-01)

Nivå

Forskarnivå

Ämne

Other social science

Betygsskala

Kraven för kursens olika betygsgrader framgår av betygskriterier, som ska finnas tillgängliga senast vid kursstart.

Kursspråk

Engelska

Behörighetskrav

Mål

After successfully participating in the course, students will be able to

• Have a general understanding of the possibilities and limitations of Machine Learning and understand core principles as well as difference between ML and Econometrics
• Have a theoretical and applied knowledge of common ML algorithms
• Recognize relevant areas of application and understand differences in applicability across algorithms (No free lunch theorem)
• Understand key evaluation methods and critically assess application cases and outcomes
• Use and apply structured and unstructured data sources, identify relevant data sources

Innehåll

• Key methods in ML and their application: supervised, unsupervised, and deep learning
• Practical applications and recent advances for causal and predictive empirical research
• Data analytics of heterogenous sources of data

Examinationsformer

- Students actively participate in the course and contribute to discussions during the course - Students conceptualize an application case and prepare a short presentation that they will pitch at the end of the course - Students write a course paper that applies methods and good practices introduced in the course - Students submit the script of the data analysis underling the course paper

Ansvarig institution eller motsvarande

Institutionen för ekonomi

Kompletterande uppgifter

Övrig information

This course is part of the research school People, Society and Sustainability, a joined research school between the Department of Economics and the Department of Urban and Rural Development.

Preparation

• Familiarize yourself with R/Python
• Read the papers and the introduction chapters of the books of the literature list
• To be adjusted