Course content

The course is open for the students who are interested in artificial intelligence (AI) and willing to apply AI to practical applications. Focus will be on principles and implementation of AI methods for maritime engineering and applications in the common maritime engineering project of the study program track. Throughout the course, students will gain the knowledge of concept, methodology and experiments from examples of real projects in marine domain. The course content are as follows:

• AI introduction
• Data collection, analysis and purification
• AI and control methods
  • supervised learning
  • unsupervised learning
  • reinforcement learning
  • deep learning…
• AI in different applications
  • Ship motion prediction
  • Engine fault diagnosis and prognosis
  • ANN-based controller for ship docking
  • Thruster fault detection and isolation
  • Deep reinforcement learning for COLREgs-compliant maneuvering
  • Sea state estimation…

Learning outcome

Knowledge and skills

• Understand AI methods, their advantages, and limitations; comprehend the scope and challenges of AI and control in maritime applications.
• Gain skills in data collection, analysis, and purification.
• Learn various AI and control methods, including supervised, unsupervised, and reinforcement learning, as well as deep learning techniques.
• Understand AI applications in maritime engineering, like ship motion prediction, engine fault diagnosis, etc.
• Contrast classical control systems with data-driven methods for a comprehensive understanding of maritime engineering challenges.

Competence

• Develop the competence to handle data effectively, formulate problems, simplify model complexity, and select appropriate AI methods for maritime engineering projects.
• Gain the ability to design and implement AI algorithms for real-world maritime applications.
• Enhance skills to work and contribute effectively as part of an interdisciplinary team, focusing on applied AI and control in maritime settings.

Learning methods and activities

Lectures, exercises and examples from variant applications will be provided in the course. There will be individual mandatory assignments and exam project related to the maritime engineering project of the study program track. 75% of the mandatory assignments have to be approved before admission to examination.

Compulsory assignments

• Mandatory assignment

Specific conditions

Recommended previous knowledge

The students are suggested to have the basic knowledge of linear algebra, statistics and some programming experience.

Required previous knowledge

None.

Course materials

• Jackson, Philip C. Introduction to artificial intelligence. Courier Dover Publications, 2019.
• Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
• Sutton, Richard S., Barto, Andrew G. Reinforcement learning: An introduction. MIT press, 2018.
• A Beginner's Guide to Deep Reinforcement Learning, https://pathmind.com/wiki/deep-reinforcement-learning