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Best Practice - Machine Learning for Ship Autonomy



Course format On-site
Date 2020-08-17 - 2020-12-20

In this course, we will introduce variant of machine learning methods and apply them for ship autonomy applications. The aim is to show the potential use of these methods for solving specific problems on autonomous ships, such as path planning, auto-docking and motion prediction. We plan to present case studies for each of introduced machine learning methods.

  • Introduction to machine learning (state of the art)
  • Dijkstra method, A* method (application: path planning for close-range maneuvering)
  • Neural network architecture, including MLP, LSTM and NARX (application: ship motion prediction, and force allocation to thrusters)
  • Deep learning method (application: remaining useful life predictions for turbofan engine)


Admission to a programme of study is required:

  • Naval Architecture (850MD)
  • Naval Architecture (850ME)
  • Product and System Design (840MD)
  • Product and System Design (845ME)
Recommended previous knowledge

Students must have basic knowledge of machine learning, especially neural networks. Background in Product Design, Automation or Computer Science is also an advantage.

Learning outcomes

After course completion, the student should understand the concept of machine learning and autonomy in ship systems, as well as having knowledge of important machine learning algorithms and techniques, specially applied to maritime cases.

The student develops skills in planning, design and applying machine learning techniques to maritime cases.

The student is able to formulate research problems involving machine learning apply its principles in complex systems, such as maritime.


ISCED Categories

Machinery and operators