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Course plan

Credits: 3 ECTS (+2 optional ECTS)

Grade: Fail/Pass

English name: Applications of Machine Learning in Water Contexts

Swedish name: Tillämpning av maskininlärning i vattensammanhang

Intended learning outcomes:

Upon completing this course, students should be able to:

  • Explain the basics of machine learning (ML) and its applications.
  • Develop ML-based frameworks for modeling water-related issues.
  • Use Python programming language to handle and analyze data.
  • Train ML algorithms like random forests using Python libraries including NumPy and scikit-learn.
  • Evaluate the performance of the ML algorithms.
  • Interpret and discuss the results of ML algorithms.

Teaching activities:

Lectures, tutorial sessions, guest lectures, assignments, and seminars.


The examination is based on one assignment (plus an optional assignment for two additional ECTS). The participant must attend all scheduled course activities and actively participate in discussions.


This course is open to Ph.D. students and participants from the water sector.


Assistant Professor Amir Naghibi, LTH Lund University


Assistant Professor Amir Naghibi, LTH Lund University and others. Contact:


Provided during course

Page Manager: | 2022-11-18