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
Ongoing PhD studies in social sciences/ business studies/ economics (interested students in related fields are subject to agreement). Students should have basic knowledge of programming language (R/ Python). No prior knowledge of ML required.
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