Introduction to Machine Learning in Agricultural Economics

3.5 hp

2026-05-26 - 2026-06-11

Uppsala

English

Application Code: SLU-F0045

Course Code: P000171

Application deadline: 2026-04-15

Course facts

Subject: Other social science
Credits: 3.5 credits
Course code: P000171
Application deadline: 2026-04-15
Study location: Uppsala
Form of instruction: Campus-based instruction
Course language: English
Responsible Department/Equivalent: Department of Economics
Rate of study: 100 %

P000171, Introduction to Machine Learning in Agricultural Economics, 3.5 Hp
Print syllabus

Syllabus

Finalized by: Jens Rommel, 2026-02-03
Valid from : First half-year 2026 (2026-01-01)

Level

Third cycle

Subject

Other social science

Grading Scale

The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.

Course language

English

Entry Requirements

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.

Objectives

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

Content

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

Examination Formats and Requirements for Passing the Course

- 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

Responsible Department/Equivalent

Department of Economics

Supplementary information

Other 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