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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.
  • Examination: 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.
  • Prerequisites: This course is open to Ph.D. students and participants from the water sector.
  • Examiner: Assistant Professor Amir Naghibi, LTH Lund University
  • Teachers: Assistant Professor Amir Naghibi, LTH Lund University and others. Contact: amir [dot] naghibi [at] tvrl [dot] lth [dot] se (Amir[dot]Naghibi[at]tvrl[dot]lth[dot]se)
  • Literature: Provided during course
Last updated: 12 Jun 2025 | babs [dot] kunle [at] tvrl [dot] lth [dot] se