- has an basic understanding of AI and the history of AI.
- has knowledge of where AI is used and its development today.
- has an basic understanding of different machine learning algorithms and their future possibilities
- can recognize different inputs used in AI and machine learning
- recognises the possibilities to get information and data from different systems and how Human - Machine Interaction is adapted in autonomous vessels
Content of the study unit
- Administrative matters
- Computational thinking and algoritms - What is computing? Algorithms and complexity
- Introduction to AI and Autonomy - What is AI? How do you define Autonomy?
- Agents and Search - How to solve problems with “Good old-fashioned AI”
- Introduction to Machine Learning - Overview of ML, Risks and Problems with ML
- Supervised learning - Basic supervised learning through regression
- Machine vision - Deep neural networks, Image segmentation, Image detection, Image recognition
- Reinforcement learning - Reinforcement learning as search, Autonomy and reinforcement learning
- Industrial Internet - What is IoT? What is a Digital Twin?
- Sensors and Sensor fusion - Situational awareness, LIDAR, IR, GNSS and IMU’s
- Autonomy and Safety - Software safety, Liability, Accountability
Assessment criteria – satisfactory (1-2)
Theory and methodology are poorly understood and implemented in Autonomous Maritime Operation related tasks/ assignments.
Research, communication and documentation are hardly acceptable.
Appear to grasp theory and have made a start in showing its applicability in Autonomous Maritime Operation related tasks/ assignments. Research, communication and documentation are acceptable.
Assessment criteria – good (3-4)
Understanding of theory and applicability of methods in Autonomous Maritime Operation related tasks/ assignments, but work could be stronger.
Research, service design, communication and documentation are good.
Very Good 4
General understanding of theory and methods, very good implementation in Autonomous Maritime Operation related tasks/ assignments.
Reliable research, innovative service design and communication and documentation on good level.
Very active participation.
Assessment criteria – excellent (5)
Mastery of theory and methods, proficiency of implementation of them in Autonomous Maritime Operation related tasks/ assignments.
Outstanding research, innovative service design and excellent communication and documentation.
Very active participation.
Name of lecturer(s)
10.02.2022 - 30.04.2022
Enrollment date range
15.11.2021 - 31.03.2022
Faculty of Technology and Seafaring
Teachers and responsibilities
Degree Programme in Autonomous Maritime Operations