Introduction to Marine AI Course
This course introduces you to artificial intelligence and how it is changing the maritime sector. You will learn the key concepts of AI and machine learning, how autonomous navigation systems perceive, decide and control, how AI predicts failures and supports fleet management, how it optimizes routes using weather and traffic data, and what challenges, regulations and ethical questions come with implementing AI at sea.
Prior knowledge check
These are the skills this program gives you. Tick the ones you already master and we will tell you which part of the syllabus is for you.
You are starting from scratch in this subject. The program is designed precisely for that.
What is the Introduction to Marine AI Course?
This short course gives you a solid foundation to understand and apply AI technologies in the marine environment. You will study the definition and types of AI, its history in the maritime industry, machine learning, neural networks and evolutionary and optimization algorithms, enabling technologies such as sensors, IoT, big data and cloud platforms, levels of autonomy, perception with radar, LIDAR, sonar and computer vision, decision-making and obstacle avoidance, predictive maintenance with vibration, temperature and pressure data, fleet management software, route optimization algorithms, AI weather prediction and fuel-saving routes.
What sets this course apart: it is designed as an accessible entry point for maritime professionals and technical profiles alike, explaining AI through the problems it solves on board and ashore; it covers the full range of applications, from autonomy to maintenance and routing; it addresses the practical barriers of cost, infrastructure and interoperability; and it closes with legal, regulatory and ethical aspects and a look at where marine AI is heading.
Program highlights
- AI fundamentals: machine learning, neural networks and optimization algorithms.
- Autonomous navigation: levels of autonomy, perception, decision and control.
- Predictive maintenance: sensor data, anomaly detection and fleet software.
- AI route optimization: weather, traffic, fuel savings and real-time adjustment.
- Implementation: integration with existing systems, costs and interoperability.
- Regulation and ethics: legal aspects of autonomous decisions and future trends.
Syllabus of the Introduction to Marine AI Course
5 modules. Select a module to see its content.
Fundamentals of artificial intelligence and its application in the marine sector
- 1.1. Introduction to artificial intelligence (AI): definition, types of AI (weak and strong) and general applications
- 1.2. History of AI in the maritime world: evolution of technologies and their impact on the maritime industry
- 1.3. Key concepts in AI: machine learning, neural networks, evolutionary algorithms and optimization algorithms
- 1.4. Applications of AI in the maritime sector: route optimization, predictive maintenance, autonomous navigation and more
- 1.5. Enabling technologies for marine AI: sensors, IoT, big data and cloud platforms
Autonomous navigation and AI systems for vessels
- 2.1. Principles of autonomous navigation: definition, components and levels of autonomy, from assisted control to no human intervention
- 2.2. Sensors and perception systems: radar, LIDAR, sonar, cameras and computer vision systems for autonomous navigation
- 2.3. Decision-making algorithms for autonomous navigation: route planning, obstacle avoidance and trajectory control
- 2.4. Integration of AI with control systems: navigation control and autonomous response to changing conditions
- 2.5. Simulation and testing of autonomous navigation: verification, validation and certification of autonomous systems
Predictive maintenance and fleet management with AI
- 3.1. Predictive maintenance in the maritime sector: how AI can predict failures and optimize vessel maintenance scheduling
- 3.2. Sensors for continuous monitoring: analysis of vibration, temperature, pressure and other metrics to detect anomalies
- 3.3. Analysis of historical data for failure prediction: how AI uses large volumes of data to anticipate repair needs
- 3.4. Fleet management software: platforms that integrate AI to optimize the management and maintenance of maritime fleets
- 3.5. Reducing operating costs with AI: resource optimization, reduced downtime and more efficient operations
Marine route optimization with AI
- 4.1. Marine route optimization algorithms: definition, types of algorithms and their application in the maritime industry
- 4.2. Factors affecting route planning: weather conditions, maritime traffic, fuel costs and operational efficiency
- 4.3. Use of AI to predict weather and marine phenomena: integration of real-time weather data into decision-making
- 4.4. Fuel-saving routes: how AI helps choose the most efficient routes and reduce environmental impact
- 4.5. Real-time monitoring: how AI adjusts routes in response to changing conditions such as storms, currents and traffic congestion
Implementing AI in the maritime sector: challenges and future
- 5.1. Design and implementation of AI systems in the maritime sector: steps, tools and platforms needed for successful implementation
- 5.2. Integration of AI with existing infrastructure: how traditional navigation and control systems integrate with AI-based solutions
- 5.3. Challenges in adopting AI in the maritime sector: costs, lack of infrastructure, interoperability and resistance to change
- 5.4. Legal and regulatory aspects: maritime law, regulation of autonomous AI and ethical aspects of autonomous decision-making
- 5.5. The future of AI at sea: advances in artificial intelligence, autonomous vehicles and the evolution towards the smart ship
Duration and format of the Introduction to Marine AI Course
It is 300 hours. Tell us how many you can study each week and we will tell you when you will really finish.
This is the pace at which most students finish. It fits well with the installment plan.
Academic leadership and faculty
The Navalis teaching team: working professionals who teach and supervise the programs.
Dr. Marina AlcoverAcademic Director and Head of Quality Assurance
Eng. Diego CifuentesSenior Lecturer in Naval Welding and Non-Destructive Testing; Head of QA/QC for Welded Joints
Capt. Javier Abaroa (MCA)Senior Lecturer in Ship Handling, Electronic Navigation (ECDIS/Radar/ARPA) and GMDSS; STCW Compliance and Bridge Resource Management Coordinator
Dr. Pau FerrerSenior Lecturer in Naval Architecture and Simulation; Director of the Hydrodynamics and CFD Laboratory
Dr. Nuria LlobregatSenior Lecturer in Marine Meteorology, SAR and Operational Safety; Navigation Risk Management Coordinator
Eng. Javier BañulsSenior Lecturer in Electrical Engineering, Automation and ETO Engineering; Electronic Integration and Engine Alarms Coordinator
Eng. Sofía MarquinaSenior Lecturer in Materials, Composites and Corrosion Protection; Coordinator of the Advanced Paints and Coatings Line
Eng. Tomás RieraSenior Lecturer in Propulsion, Alternative Energy and Hybrid Systems; Energy Performance and Vibration CoordinatorObjectives of the Introduction to Marine AI Course
Career opportunities after the Introduction to Marine AI Course
Questions about the Introduction to Marine AI Course
The application of artificial intelligence to the problems and challenges of the ocean and the maritime industry, from navigation and maintenance to environmental monitoring.
Route optimization to cut fuel use and emissions, predictive maintenance, autonomous navigation, object detection, fleet management and environmental risk prediction, among others.
Basic Python helps you get more from the practical examples, but the course is introductory and is also suitable for maritime professionals without a programming background.
Yes. The final module addresses maritime law, regulation of autonomous AI and the ethical questions raised by autonomous decision-making.
You will understand the main AI techniques used in the maritime sector, evaluate where they add value and take part in AI projects with a clear view of their requirements and limits.
Fully online, through a virtual campus available 24/7, with live sessions that are recorded and tutorials with the teaching staff.
The course comprises 300 hours of study across 5 modules of about 60 hours each and closes with a module on the challenges and future of AI implementation. You can progress at your own pace within the schedule set when you enroll.
More programs in Innovation and startups
Admission process
No waiting for a call back. If your documents are in order, you can enroll in the same session.
- Profile: aimed at naval engineers and naval architects, merchant navy officers and fleet operators, software developers and data scientists interested in marine applications, researchers in ocean science and environmental protection, and engineering and marine science students; basic programming knowledge, preferably Python, or professional experience in the maritime sector is recommended.
- Language: sufficient command of English to follow the classes and the technical documentation.
- Technical resources: a computer with a stable internet connection, an up-to-date browser and a Python environment such as Anaconda; a GPU is useful but not required.
- Admission: online application, review of your profile and a short interview with the academic team.
- 01ApplicationYour details, background and documents.
- 02Instant reviewWe check that everything is complete and in order.
- 03EnrollmentChoose full payment or instalments and reserve your place.
Admission is provisional until the committee validates the documents provided. If you do not meet the requirements, the full amount is refunded.
Ask us before you enroll
Leave your details and admissions will write to you to answer any questions. If you are already sure, you can apply and pay in the same session.