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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.Naghibi@tvrl.lth.se

Literature:

Provided during course

Page Manager: catherine.paul@tvrl.lth.se | 2022-11-18