Master’s Degree in Machine Learning for Maritime Safety
Why this master’s programme?
The Master’s in Machine Learning for Maritime Security
This program equips you to protect the maritime sector from 21st-century threats. You will learn to detect anomalous patterns in navigation data, predict cybersecurity risks on ships and port infrastructure, and optimize emergency response in real time. This program combines machine learning theory with practical case studies from the maritime sector, providing you with the skills necessary to lead the digital transformation of security in this crucial industry.
Differentiating Advantages
- Focus on maritime applications: detection of illegal fishing, analysis of smuggling routes, prediction of equipment failures.
- Real-world industry data: access to anonymized datasets of maritime traffic, meteorology, and cybersecurity.
- Cutting-edge tools: hands-on learning with Python, TensorFlow, Keras, and other key libraries.
- Customized projects: development of innovative solutions for real-world challenges faced by maritime companies and organizations.
- Specialized Networking: Connecting with industry experts, participating in events, and finding job opportunities.
- Modality: Online
- Level: Masters
- Hours: 1600 H
- Start date:
Availability: 1 in stock
Who is it aimed at?
- Maritime security professionals seeking to apply advanced data analytics techniques for risk prevention and resource optimization.
- Engineers and data scientists interested in specializing in the maritime sector, exploring machine learning applications in safety and operational efficiency.
- Naval and coast guard officers wishing to improve threat detection, traffic pattern analysis, and incident response using predictive models.
- Fleet management and marine insurance managers seeking to reduce costs, optimize routes, and predict equipment failures through data analytics.
- Researchers and academics wishing to contribute to the development of innovative solutions for maritime security using the latest Machine Learning tools.
Flexibility and Practical Application
Master’s program designed for active professionals: adaptable online format, projects based on real-world cases, and focus on the practical implementation of the knowledge acquired.
Objectives and skills
Detect and predict suspicious activities in real time:
Analyze anomalous behavior patterns using AI and correlate with intelligence data, alerting about potential threats to maritime and port security.
Optimizing risk management in maritime operations:
“Implement contingency plans and communicate effectively in emergency situations, prioritizing the safety of human life at sea and the protection of the environment.”
Automating the identification of anomalies in maritime traffic:
Analyze historical and real-time maritime behavior patterns, using Machine Learning algorithms to detect significant deviations and generate automatic alerts.
Develop predictive models for the prevention of maritime incidents:
Integrate AIS, meteorological, and historical incident data to identify risk patterns and generate early warnings.
Improving efficiency in responding to maritime emergencies:
Mastering search and rescue (SAR) techniques, optimizing communication with coordination centers and effectively using available onboard and external resources.
Strengthening the cybersecurity of critical maritime infrastructure:
Implement a robust vulnerability management and incident response program, prioritizing network segmentation and protection of SCADA/ICS systems with continuous monitoring and proven contingency plans.
Study plan – Modules
- Mathematical and statistical foundations in Machine Learning applied to maritime threat detection: probability theory, linear algebra, and multivariate statistics
- Design and implementation of supervised models for classification and detection of hostile objects in maritime environments: Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs)
- Application of unsupervised algorithms for anomaly detection in navigation patterns and maritime traffic: hierarchical clustering, DBSCAN, and density-based methods
- Time series and deep learning models for prediction and early detection of dynamic threats: LSTM, GRU, and hybrid models with time-sensitive focus
- Integration of multisensor data (AIS, radar, satellite imagery, IoT sensors) to improve detection and classification accuracy using data fusion techniques
- Advanced image and video processing algorithms applied to Maritime surveillance: semantic segmentation, motion detection, and recognition of anomalous behavior.
Optimization of algorithms for real-time execution on board maritime platforms: reduction of computational complexity and use of specialized hardware (FPGA, GPU).
Study and application of federated learning techniques for the protection of sensitive data in collaborative maritime security ecosystems.
Implementation of early warning systems with probabilistic classification algorithms and Bayesian models for risk and threat assessment.
Performance evaluation, specific metrics, and validation of ML systems in real-world maritime navigation scenarios: accuracy, recall, F1 score, and ROC curves.
Case studies applied to forensic analysis and detection of illicit activities such as piracy, smuggling, and stealth navigation using advanced machine learning.
International regulations and standards for the application of artificial intelligence in maritime security: compliance with IMO, SOLAS, and other protocols. cybersecurity
- Fundamentals of deep neural networks applied to maritime safety: CNN, RNN, and Transformer architectures focused on multidimensional data from nautical sensors
- Predictive modeling of critical events in maritime environments: advanced time series techniques, anomaly detection, and supervised classification for incident prevention
- Integration of heterogeneous data: sensor fusion of AIS, radar, satellites, and IoT systems to feed robust machine learning models
- Development and optimization of algorithms for early risk identification: overfitting, underfitting, regularization, and cross-validation specific to maritime scenarios
- Implementation of proactive alert systems based on artificial intelligence predictions: distributed architecture, low latency, and real-time response
- Simulation and analysis of risk scenarios using reinforcement learning for mitigation tactics and automated responses in maritime operations
- Optimization
- Use of parameters and neural networks using Bayesian tuning techniques and genetic algorithms oriented towards operational safety
- Use of hybrid models combining classical machine learning and neural networks to increase interpretability and accuracy in risk detection
- Cybersecurity applied to maritime predictive systems: data security, prevention of adversary attacks, and robustness of AI models against specific vulnerabilities
- Performance evaluation and specific metrics for predictive models in maritime safety: accuracy, recall, F1-score, ROC-AUC, and analysis of real-world case studies
- Deep Learning Fundamentals Applied to Naval Surveillance Systems: Neural Architectures and Their Particularities in Maritime Environments
- Advanced Preprocessing of Multimodal Data: Satellite Imagery, Radar, and Acoustic Sensors for Efficient Threat Detection
- Design and Implementation of Convolutional Neural Networks (CNNs) for the Automatic Identification of Objects and Anomalies in Maritime Scenes
- Recurrent Models (RNNs, LSTMs) and Their Application in the Sequential Analysis of Naval Traffic Data and Behavioral Patterns
- Transfer Learning and Fine-Tuning in Deep Networks to Optimize Threat Classification with Limited Datasets
- Integration of Computer Vision Systems with Semantic Segmentation Algorithms for the Accurate Recognition of Suspicious Vessels
- Methodologies for Training with Unbalanced Databases and Data Augmentation Techniques Specific to Maritime Scenarios
- Rigorous performance evaluation: advanced metrics, cross-validation, and testing in simulated naval surveillance environments
- Implementation of real-time inference pipelines: hardware acceleration, optimization, and deployment on shipboard platforms
- Cybersecurity in deep learning models applied to critical systems: protection against adversarial attacks and data manipulation
- Case studies and integrated projects: automated detection of piracy, smuggling, and illicit activities in the maritime domain
- Future trends and challenges: fusion of multisensory data, multimodal models, and deep reinforcement learning for naval defense
- Design and architecture of Machine Learning platforms specifically for maritime environments: hardware components, software integration, and cloud, edge, and fog computing topologies for performance and latency optimization
- Multisensor fusion: advanced algorithms for integrating data from radar, AIS, sonar, infrared cameras, and satellites, ensuring robustness, temporal synchronization, and spatial coherence in complex maritime scenarios
- Edge-to-Cloud processing: strategies for distributed deployment of ML models, including real-time inference on edge devices and continuous training in the cloud using hybrid architectures and containers
- Cybersecurity in AI platforms for maritime security: mechanisms for protecting sensitive data, intrusion detection, secure access control, and resilience against adversarial attacks in machine learning models
- Rigorous operational validation: methods for evaluating and benchmarking ML models under real maritime conditions, using synthetic and real datasets, and metrics for
- Accuracy, recall, F1-score, and false positive/negative analysis for critical systems
- Explainable AI (XAI) implementation for naval operations: model interpretation techniques, visualization of automated decisions, and generation of understandable reports for operators and commanders in real time
- Orchestration and automation of maritime data pipelines: integration of ETL flows, quality control, logging, and continuous monitoring to ensure data integrity and availability in harsh maritime environments
- Regulations and standards for ML platforms in maritime security: compliance with international guidelines, maritime certifications, and operational requirements for deployments in commercial and military fleets
- Deployment case studies: detailed study of successful platforms deployed in coastal surveillance, intrusion detection, accident prevention, and maritime emergency response
- Model lifecycle management: from initial design, training, evaluation, continuous updating, and decommissioning, ensuring robustness and adaptability and preventive maintenance in naval operating environments
- Fundamentals of mathematical optimization applied to intelligent maritime systems: linear, nonlinear, and mixed-integer programming
- Predictive models for maritime incident management: regression, classification, and time series in high-uncertainty contexts
- Deep learning algorithms for early detection of anomalies and critical patterns in sensor and satellite data
- Integration and fusion of multisensor data in real time: AIS, radar, echo sounder, thermal cameras, and UAV systems
- Advanced signal processing and adaptive filtering techniques for noise reduction in harsh maritime environments
- Intelligent control systems based on reinforcement learning algorithms for autonomous emergency response
- Design and evaluation of distributed architectures for edge computing and cloud computing for maritime systems
- Optimization Multi-objective approach to maritime emergency resource allocation: minimizing response times and maximizing territorial coverage
Implementation of predictive analytics and stochastic simulation techniques to anticipate the evolution and consequences of maritime incidents
Development of intelligent dashboards with advanced visualization for real-time monitoring and data-driven decision-making
Protocols and standards for the interoperability of intelligent systems in port and maritime infrastructure
Application of advanced cybersecurity methods to protect the integrity and availability of critical systems against cyberattacks
Performance evaluation and validation of machine learning models in maritime operational environments using metrics of accuracy, recall, and response time
Case studies and real-world simulations of maritime incidents addressed by intelligent systems with real-time optimization
International regulations and their impact on the implementation of AI-based security solutions maritime
- Advanced Fundamentals of Machine Learning in Maritime Environments: Supervised, Unsupervised, and Reinforcement Paradigms Applied to Safety
- Deep Neural Network Architectures for Anomaly Detection in Maritime Traffic and Multisensor Surveillance Systems
- Predictive Modeling Using Time Series: LSTM, GRU, and Transformers to Anticipate Critical Events such as Collisions, Groundings, and Spills
- Multimodal Data Integration: Radar Sensors, AIS, Satellite Imagery, and Oceanographic Data in Machine Learning Models
- Optimization of Real-Time Detection Algorithms: Dimensionality Reduction, Feature Selection, and Parallel Processing Techniques
- Implementation of Early Warning Systems Using Clustering and Hierarchical Classification Techniques for the Identification of Threats and Irregular Behaviors
- Algorithms Adaptive models for dynamic surveillance of sensitive shipping lanes and high-traffic areas under changing environmental conditions.
Performance evaluation and validation of models using specialized metrics for marine environments, including sensitivity, specificity, and false positive rates.
Application of federated learning and differential privacy for secure handling of sensitive data in marine multinodal networks.
Advanced case studies: intrusion detection, monitoring of unregistered vessels, and identification of illegal fishing patterns using hybrid ML and geospatial analysis techniques.
- Deep Learning Fundamentals Applied to Maritime Security Systems: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers for Real-Time Data Analysis
- Design and Implementation of Hybrid Architectures: Integration of Classical Machine Learning Algorithms with Deep Learning Models to Improve Early Detection
- Advanced Development Platforms and Environments: Use of TensorFlow, PyTorch, and Edge Computing Platforms for Deployment in Maritime Environments
- Multisensor Data Processing and Analysis: Integration of Radar, Sonar, AIS, and Optical Camera Signals Using Data Fusion Techniques
- Automatic Threat Detection Algorithms: Real-Time Identification of Unauthorized Objects, Suspicious Patterns, and Illicit Activities
- Automatic Response Systems Based on Intelligent Agents and Business Rules: Development of Action Protocols for Detected Incidents
- Optimization and Scalability of Platforms for Operation in Adverse maritime conditions: robustness, latency, and energy consumption
Implementation of training pipelines and continuous validation using federated learning techniques for the protection of sensitive data
Model performance evaluation: specific metrics for detection and classification in maritime scenarios, including the handling of false positives and negatives
Cybersecurity and protection of artificial intelligence infrastructures in maritime environments: strategies to mitigate attacks and vulnerabilities in real time
- Mathematical and statistical foundations applied to machine learning: probability, inferential statistics, linear algebra, and differential calculus
- Artificial intelligence models for critical systems: differentiation between supervised, unsupervised, and reinforcement learning in maritime environments
- Deep neural networks and advanced architectures: CNNs, RNNs, LSTMs, and Transformers for sequential and spatial analysis of maritime data
- Integration of multimodal sensors: acquisition, fusion, and preprocessing of data from radar, AIS, sonar, thermal cameras, and maritime meteorology
- Anomaly detection and classification algorithms: autoencoders, Isolation Forest, and density-based methods for identifying operational risks in real time
- Advanced predictive modeling: time series techniques, Prophet, ARIMA, and deep learning models for predicting events such as collisions, structural failures, and extreme weather conditions
- AI-based early warning systems: design, implementation, and evaluation of inference pipelines for rapid response to critical events
- Optimization of maritime routes and logistics using genetic algorithms, swarm intelligence, and reinforcement learning to minimize risks and fuel consumption
- Model performance evaluation: accuracy metrics, recall, F1 score, ROC curves, and cross-validation to ensure robustness and reliability in operational scenarios
- Implementation of AI systems in high-availability, low-latency environments: edge computing infrastructure, containers, and deployment on maritime platforms
- Cybersecurity in AI systems for maritime security: defense techniques against adversaries, detection of data attacks, and robustness against adversarial disruptions
- Ethical and regulatory analysis of AI use in the maritime sector: regulatory compliance, data privacy, and accountability in automated decision-making
- Mathematical Foundations of Permutations in Maritime Sequence Analysis: Combinatorial Theory Applied to Temporal and Spatial Events
- Probabilistic Models for the Automatic Detection of Anomalous Patterns in Multivariate Marine Data
- Advanced Architectures of Deep Neural Networks: CNNs, RNNs, LSTMs, and Transformers Applied to Nautical Time Series
- Integration of Heterogeneous Data: AIS Sensors, Radar, Satellites, and IoT Systems for Deep Learning Model Training
- Preprocessing and Normalization of Maritime Data: Noise Reduction, Value Imputation, and Specialized Data Augmentation Techniques
- Permutation Algorithms for Threat Detection: Identification of Outliers in Maritime Traffic and Vessel Behavior
- Deployment of Deep Learning Models in Resource-Constrained and Critical Latency Environments: Edge Computing and Inference Optimization
- Evaluation and validation of automated early warning systems using specialized metrics (ROC, F1-Score, Matthews Correlation Coefficient)
- Case studies: detection of intrusions, piracy, and illicit activities using automated pattern recognition in complex maritime data
- Maritime security regulations and standards applicable to the implementation of AI and deep learning-based systems
- Problem Definition: Contemporary Maritime Threats and Requirements for Automatic Detection Systems
- System Architecture: Modular Design for Integration of Multisensor Sources (Radar, AIS, Satellites, Acoustic Sensors)
- Data Preprocessing: Advanced Cleaning, Fusion, and Normalization Techniques to Ensure Real-Time Quality
- Machine Learning Models: Selection, Training, and Validation of Deep Neural Networks, SVMs, and Ensemble Algorithms Applied to Anomaly Detection
- Implementation of Explainable AI (XAI) Models: SHAP, LIME, and Rule-Based Methods for Transparent Decision Interpretation in Critical Environments
- Development of Automatic Response Algorithms: Alerting Protocols, Threat Classification, and Real-Time Generation of Mitigation Actions
- Optimization and Performance Evaluation: Specific Metrics (Accuracy, Recall, F1 Score, AUC-ROC) in High-Uncertainty Scenarios Operational
Integration with existing systems: interoperability with maritime platforms, traffic management, and national security systems
Simulation and testing in virtualized environments: creation of synthetic scenarios and use of digital twins for robust validation
Ethical and regulatory considerations: compliance with international regulations, data protection, and safeguards against cyberattacks
Career prospects
“`html
- Maritime Security Data Analyst: Detection of anomalous behavior patterns in maritime traffic, risk analysis, and incident prediction.
- Maritime Security Machine Learning Model Developer: Creation and optimization of algorithms for fraud detection, prediction of cyberattacks on ships, and route optimization.
- Maritime Security Consultant specializing in Machine Learning: Advising companies in the maritime sector on the implementation of security solutions based on Machine Learning, risk assessment, and contingency plan design.
- Maritime Security and Machine Learning Researcher: Development of new techniques and algorithms to improve maritime security, participation in research and development projects.
- Maritime Cybersecurity Specialist: Protection of ship and port infrastructure computer systems against cyberattacks, analysis of Vulnerabilities and development of security solutions.
- Maritime Security Project Manager with Machine Learning: Leadership and management of projects for the development and implementation of security solutions based on Machine Learning, coordination of multidisciplinary teams.
- Maritime Intelligence Officer: Analysis of information for the identification of threats and risks to maritime security, preparation of reports and alerts.
- Expert in Spill and Marine Pollution Detection: Development of Machine Learning models for the early detection of spills and the prediction of their spread.
“`
Entry requirements
Academic/professional profile:
Bachelor’s degree in Nautical Science/Maritime Transport, Naval/Marine Engineering or a related qualification; or proven professional experience on the bridge/in operations.
Language proficiency:
Functional Maritime English (SMCP) recommended for simulations and technical materials.
Documentation:
Updated CV, copy of qualification or seaman’s book, national ID/passport, motivation letter.
Technical requirements (for online):
Device with camera/microphone, stable internet connection, monitor ≥ 24” recommended for ECDIS/Radar-ARPA.
Admissions process and dates
Online
application
(form + documents).
Academic review and interview
Admissions decision
Admissions decision
(+ scholarship offer if applicable).
Place reservation
(deposit) and enrolment.
Induction
(access to the virtual campus, calendars, simulator guides).
Scholarships and financial support
- Advanced Predictive Analytics: Master Machine Learning algorithms for early threat detection and maritime security optimization.
- Comprehensive Maritime Security: Apply AI techniques to protect vessels, ports, and shipping lanes against diverse risks.
- Geospatial Data Visualization: Learn to interpret and present complex data related to maritime security on interactive maps.
- Real-World Case Studies: Work with simulations and case studies to apply your knowledge to real-world industry situations.
- Maritime Industry Experts: Learn from professionals with experience in maritime security and Machine Learning Boost your career at the forefront of maritime safety with the power of Machine Learning.
Testimonials
I applied the machine learning algorithms I learned during my master’s degree to develop a maritime traffic anomaly detection system. This system, implemented in real time at the Port of Rotterdam, reduced false alarms for suspicious activity by 15%, optimizing surveillance and freeing up resources for real security cases.
I applied the knowledge gained from my master’s degree to develop a system for predicting optimal routes for a fleet of cargo planes, reducing fuel consumption by 12% and increasing the efficiency of goods delivery by 15%. This resulted in significant cost savings for the company and a reduction in our carbon footprint.
I applied the machine learning algorithms I learned during my Master’s program to develop a real-time anomaly detection system for ships in the Port of Rotterdam. This system reduced false positives by 70% and enabled the early identification of suspicious activity, significantly improving port safety and efficiency.
I applied the machine learning algorithms I learned during my master’s degree to develop a maritime traffic anomaly detection system. This system, implemented in real time by a major shipping company, reduced false security alarms by 15% and increased the efficiency of detecting illicit activities by 20%.
Frequently asked questions
Maritime safety
Yes. The itinerary includes ECDIS/Radar-ARPA/BRM with harbor, ocean, fog, storm, and SAR scenarios.
Online with live sessions; hybrid option for simulator/practical placements through agreements.
Predicting and preventing collisions by analyzing radar, AIS, and weather data.
Recommended functional SMCP. We offer support materials for standard phraseology.
Yes, with a relevant degree or experience in maritime/port operations. The admissions interview will confirm suitability.
Optional (3–6 months) through Companies & Collaborations and the Alumni Network.
Simulator practice (rubrics), defeat plans, SOPs, checklists, micro-tests and applied TFM.
A degree from Navalis Magna University + operational portfolio (tracks, SOPs, reports and KPIs) useful for audits and employment.
- Problem Definition: Contemporary Maritime Threats and Requirements for Automatic Detection Systems
- System Architecture: Modular Design for Integration of Multisensor Sources (Radar, AIS, Satellites, Acoustic Sensors)
- Data Preprocessing: Advanced Cleaning, Fusion, and Normalization Techniques to Ensure Real-Time Quality
- Machine Learning Models: Selection, Training, and Validation of Deep Neural Networks, SVMs, and Ensemble Algorithms Applied to Anomaly Detection
- Implementation of Explainable AI (XAI) Models: SHAP, LIME, and Rule-Based Methods for Transparent Decision Interpretation in Critical Environments
- Development of Automatic Response Algorithms: Alerting Protocols, Threat Classification, and Real-Time Generation of Mitigation Actions
- Optimization and Performance Evaluation: Specific Metrics (Accuracy, Recall, F1 Score, AUC-ROC) in High-Uncertainty Scenarios Operational
Integration with existing systems: interoperability with maritime platforms, traffic management, and national security systems
Simulation and testing in virtualized environments: creation of synthetic scenarios and use of digital twins for robust validation
Ethical and regulatory considerations: compliance with international regulations, data protection, and safeguards against cyberattacks
Request information
Complete the Application Form.
Attach your CV/degree certificate (if you have it to hand).
Indicate your preferred cohort (January/May/September) and whether you would like the hybrid option with simulator sessions.
An academic advisor will contact you within 24–48 hours to guide you through the admission process, scholarships, and compatibility with your professional schedule.
Faculty
Eng. Tomás Riera
Full Professor
Eng. Tomás Riera
Full Professor
Naval engineer specializing in the selection, integration, and optimization of propulsion systems for yachts, high-speed craft, and service vessels.
Eng. Sofía Marquina
Full Professor
Eng. Sofía Marquina
Full Professor
Materials engineer specializing in marine composites and corrosion protection systems for ships, yachts, and port structures.
Eng. Javier Bañuls
Full Professor
Eng. Javier Bañuls
Full Professor
Naval electrical engineer with over fifteen years of experience in the design, integration, and operation of power and automation systems.
Dr. Nuria Llobregat
Full Professor
Dr. Nuria Llobregat
Full Professor
Specialist in meteorological and operational decision-making for coastal and offshore navigation, integrating wind, wave, and current models.
Dr. Pau Ferrer
Full Professor
Dr. Pau Ferrer
Full Professor
Naval engineer with a PhD in applied hydrodynamics and over a decade of experience designing high-performance hulls and marine structures.
Cap. Javier Abaroa (MCA)
Full Professor
Cap. Javier Abaroa (MCA)
Full Professor
MCA-certified captain with command of ocean-going vessels, specializing in advanced maneuvering, navigation management, and bridge safety.