Course in Artificial Intelligence Applied to Ports
Online 300-hour course in artificial intelligence applied to ports. 6 modules covering Regulation (EU) 2024/1689 on Artificial Intelligence, Regulation (EU) 2016/679 GDPR and Python with scikit-learn. Personal tutor assigned and enrolment open.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Identify the data sources and processes in a port where AI can be applied.
Develop predictive models for arrivals and traffic.
Apply optimisation and reinforcement learning techniques to port resources.
Use computer vision for inspection and security.
Assess risks under the European AI Regulation.
Plan the integration and governance of AI solutions.
Develop an AI project applied to a port problem.
Who it's for
- Port data scientist who wants to specialise in artificial intelligence applied to ports.
- Port digital transformation manager who wants to specialise in artificial intelligence applied to ports.
- Logistics optimisation engineer who wants to specialise in artificial intelligence applied to ports.
- Computer vision specialist who wants to specialise in artificial intelligence applied to ports.
- Terminal operations analyst who wants to specialise in artificial intelligence applied to ports.
- AI consultant in logistics who wants to specialise in artificial intelligence applied to ports.
- Engineering or science graduates seeking to join port authorities.
What you'll work with
- Regulation (EU) 2024/1689 on Artificial Intelligence
- Regulation (EU) 2016/679 GDPR
- Directive (EU) 2022/2555 NIS2
- Regulation (EU) 2019/1239 EMSWe
- ISO/IEC 42001
- ISO/IEC 23894
- IMO ISPS Code
6 modules · 300 h
1.1Port value chain1.2Ship, cargo and land transport operations1.3TOS, PCS and VTS systems1.4AIS and call data1.5Port and traffic statistics1.6Environmental sensor data1.7Data quality and availability1.8Ethics and algorithmic transparency1.9European Regulation on Artificial Intelligence1.10Case study: port data inventory
2.1Estimated time of arrival (ETA)2.2Models using historical AIS data2.3Recurrent networks for trajectories2.4Lorry traffic prediction2.5Container volume forecasting2.6Time series with Prophet and ARIMA2.7Weather and seasonal variables2.8Assessing prediction errors2.9Just-in-time arrival2.10Practical: ETA model using AIS data
3.1Berth allocation problem3.2Crane scheduling3.3Metaheuristics and local search3.4Reinforcement learning in terminals3.5Tug and pilot allocation3.6Stevedore shift planning3.7Lorry appointment management3.8Energy optimisation of port equipment3.9Robustness under uncertainty3.10Workshop: berth allocation with OR-Tools
4.1OCR reading of containers and number plates4.2Container damage detection4.3Safety in operational areas4.4Vehicle counting and tracking4.5Terminal perimeter surveillance4.6Spill detection4.7Drones for inspection4.8Labelling and model training4.9Privacy and video surveillance4.10Laboratory: damage detector with YOLO
5.1Data architecture and MLOps5.2Integration with port systems5.3Risk management under the AI Regulation5.4Explainability for operators5.5Change management with stevedores5.6Cybersecurity of models5.7Impact indicators5.8Public procurement of AI solutions5.9Port AI roadmap5.10Case study: AI at the port of Rotterdam
6.1Problem identification6.2Analysis of available data6.3Selection of techniques6.4Model development6.5Validation with real data6.6Risk and ethics assessment6.7Integration plan6.8Estimation of benefits6.9Report and presentation6.10Final review with the tutor
How you'll learn
Fully 100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with scikit-learn, PyTorch, Google OR-Tools, Ultralytics YOLO, Apache Kafka, in free, academic or evaluation versions. Practice is based on real procedures: ETA prediction using real AIS data, berth allocation optimisation and container damage detection using computer vision. Assessment is continuous, module by module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Identify the data sources and processes in a port where AI can be applied.
Develop predictive models for arrivals and traffic.
Apply optimisation and reinforcement learning techniques to port resources.
Use computer vision for inspection and security.
Assess risks under the European AI Regulation.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Spanish language proficiency sufficient to follow technical regulatory texts.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme comprises 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
You work with Python with scikit-learn, PyTorch, Google OR-Tools, Ultralytics YOLO and Apache Kafka. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
SEIUM's own diploma upon passing the continuous assessment and the integrative project. This is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a Course or Postgraduate Diploma in the same area are recognised towards the higher-level programme. Check the recognition policy when you enrol.
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