Studieguide

Studieperiodens (kursens) lärandemål

After this course module the student is able to analyze maritime data ecosystems in autonomous operations context, apply AI to benefit situational awareness, differentiate machine learning techniques in situational awareness, evaluate autonomous decision-making and use predictive analytics, and to incorporate explainable AI build operator trust, manage cognitive workload, and ensure transparency in decision-making.

Studieperiodens (kursens) innehåll

This advanced course module provides a comprehensive exploration of maritime data ecosystems in the context of autonomous operations. It focuses on the application of artificial intelligence (AI) to enhance situational awareness, support autonomous decision-making, and foster human trust through explainable AI. Designed for master’s level students, the course integrates technical, analytical, and human-centered perspectives essential for future maritime professionals.

Some Key Topics:

Maritime Data Ecosystems
- Sources, structures, and distribution of data in autonomous maritime systems
- Data integrity, reliability, and real-time processing

AI for Situational Awareness
- Sensor fusion and data interpretation
- Leveraging AI to detect anomalies and enhance perception

Machine Learning Techniques
- Supervised vs. unsupervised learning
- Deep learning, decision trees, clustering, and their relevance to maritime operations

Autonomous Decision-Making and Predictive Analytics
- Decision models and algorithms for autonomous systems
- Predictive modeling for risk management and route optimization

Explainable AI and Human-Centered Design
- Building operator trust through transparency and interpretability
- Managing cognitive workload and ensuring clarity in AI-driven decisions

Förkunskapskrav

Other Curriculums studies must be completed before this course

Bedömningskriterier, nöjaktig (1)

50-60%=1
60-70%=2

Bedömningskriterier, goda-synnerligen goda (3-4)

70-80%=3
80-90%=4

Bedömningskriterier, berömliga (5)

90 -100%

Bedömningskriterier, godkänt/underkänt

NA

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