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