Machine learning methods have become an important tool for automation, control and data-driven decision making in an industrial context. The course aims at introducing and practicing several of these tools for system identification, classification and predictive analytics.
Knowledge and understanding
After completing the course, the student should be able to:
- identify and recognize industrial problems that are solvable through machine learning
- plan and apply the machine learning workflow to classification, regression and identification problems
- understand and explain the difference between different machine learning tasks and techniques
Skills and abilities
After completing the course, the student should be able to:
- develop and build machine learning models for different tasks
- validate and test the performance of a model
- develop hands-on experience with machine learning techniques
Evaluation ability and approach
After completing the course, the student should be able to:
- interpret the result of a machine learning experiment
- report on the result of a machine learning experiment
- assess the outcome of a machine learning experiment