Universitat Politècnica de València (UPV)
Master's Degree in Artificial Intelligence Technologies
Hangzhou, China
Master degree
DURATION
1 year
LANGUAGES
Spanish
PACE
Full time
APPLICATION DEADLINE
EARLIEST START DATE
Sep 2027
TUITION FEES
EUR 4,290
STUDY FORMAT
On-Campus
Key Summary
About: The Máster Universitario en Artificial Intelligence Technologies programme provides multidisciplinary training in AI fundamentals, techniques and applications. It covers machine learning, deep learning, computer vision, cloud computing, multi-agent systems, VR / AR, computational linguistics, data visualization and bio-inspired natural computing. You’ll learn algorithms, architectures, optimization methods, engineering ethics, scientific writing and bilingual communication. Faculty from Universitat Politècnica de València and Beihang University (ranked 28th in the 2025 Shanghai Ranking for computer science & technology) teach labs, projects and a master’s thesis.
Career Outcomes: Graduates can become Artificial Intelligence Engineer, Machine Learning Specialist or Researcher, or data scientists, developers or architects.
This official master’s degree provides 60 ECTS of multidisciplinary training in artificial intelligence technologies. Its content includes machine learning, deep learning, computer vision, cloud computing, multi-agent systems, virtual and augmented reality, computational linguistics, data visualisation, natural computing, algorithms, architectures, optimisation, engineering ethics, scientific writing, languages and communication. Teaching is provided by Universitat Politècnica de València and Beihang University and includes laboratories, projects and an individual multidisciplinary master’s thesis defended before a university panel.
The standard programme consists of one academic year, from September 2026 to July 2027, with 60 ECTS studied at the Hangzhou campus. An additional second year of 60 ECTS may be added at the Hangzhou campus to pursue a degree from Beihang University. The UPV degree is officially recognised. The programme structure comprises 30 compulsory ECTS, 18 elective ECTS and 12 master’s thesis ECTS. It has no curricular work-placement credits, although extracurricular placements may be possible with technology companies in Hangzhou, including Alibaba, DeepSeek, TikTok and Unitree.
The program addresses AI applications in healthcare, Industry 4.0, automation and digital communication, with an ethical, global and sustainable perspective. It is delivered face-to-face in English at the UPV Hangzhou Campus Site in China.
Module 1: Compulsory (minimum credits: 30)
Subject: Mathematical Tools
- Matrix Theory and Applications
- Numerical Analysis
Subject: Artificial Intelligence Techniques and Methods
- Computational Linguistics
- Deep Learning System
- Machine learning
- Multiagent Systems
Subject: Computational Techniques and Methods
- Metaheuristic techniques
- Natural computing
Module 2: Complementary Training (minimum credits: 18)
Subject: Complementary Training
- Advanced computing systems for industry
- Chinese / Spanish
- Cloud systems resource management and performance evaluation
- Data visualization
- Engineering ethics
- Parallel Processing and Architecture
- Scientific paper writing
- Virtual and augmented reality
Module 3: Master's Thesis (minimum credits: 12)
Subject: Master's Thesis
- Master's Thesis
Fundamental
- Explain the fundamental principles of machine learning and deep learning, characterising neural network models and hardware architectures to address complex problems in advanced AI systems.
- Explain the fundamental principles of computational linguistics and natural language processing to address complex problems in advanced artificial intelligence systems.
- Explain the fundamental principles of the concept of agent and multi-agent systems, as well as their dynamics for interpreting complex problems in advanced artificial intelligence systems.
- Conceptually analyse machine and deep learning techniques, neural network models, fundamentals of language processing and theories of intelligent agents, establishing relationships between algorithms, representations and architectures applicable in various computational contexts.
- Apply machine and deep learning algorithms, natural language processing techniques, and multi-agent systems to build advanced artificial intelligence solutions.
- Implement neural network models, natural language processing systems, and multi-agent architectures, using specialised software and accelerated hardware, for application in artificial intelligence systems.
- Evaluate the efficiency, adequacy, performance, and scalability of machine learning models and multi-agent systems for application in the area of artificial intelligence.
- To compare machine learning approaches, neural architectures, linguistic techniques and multi-agent mechanisms in a well-founded manner, determining their suitability for different scenarios using criteria of efficiency, generalisation, and computational feasibility.
- Explain the fundamental principles of metaheuristics and natural computing models, characterising their algorithmic foundations and their applicability to complex problems with combinatorial growth.
- Conceptually analyse data structures, bioinspired models, and search strategies, interpreting their abilities to represent, simulate, and solve computationally difficult problems in different domains.
- Apply metaheuristics and natural computing models to solve optimisation and satisfaction problems, configuring specific strategies according to the characteristics of the problem.
- Implement bio-inspired algorithms and metaheuristic techniques to address complex optimisation problems, evaluating results through controlled experimentation and comparative analysis.
- Critically evaluate the efficiency, robustness and adequacy of different heuristic and metaheuristic methods, justifying their selection based on the complexity of the problem and the quality of the solutions obtained.
- Compare various heuristic, metaheuristic and natural computation-based approaches with scientific criteria, determining their suitability for solving real problems through criteria of scalability, computational complexity and ability to explore and exploit search spaces.
- Explain the theoretical foundations of numerical methods and matrix algebra, describing their role in the computational solution of equations, functional approximations and systems modelling in artificial intelligence.
- Conceptually analyse stability, error, and convergence in numerical methods, interpreting the algebraic structure of matrices and operators to understand the computational behaviour of advanced algorithms.
- Apply numerical techniques and matrix operations to solve linear and nonlinear problems, approximate functions, and simulate relevant dynamical systems in AI and optimisation contexts.
- Implement numerical algorithms and matrix procedures in scientific programming environments, evaluating results through reproducible experimentation and comparing calculation alternatives.
- Critically evaluate the accuracy, efficiency and robustness of numerical methods and matrix operations, justifying the choice of techniques according to stability, computational cost and reliability requirements.
- To compare numerical strategies and algebraic approaches in a well-founded manner in order to select the most appropriate technique for different computational problems, considering criteria of performance, sensitivity and quality of the solutions.
- Integrate and autonomously apply the knowledge, techniques and skills acquired in the master's degree to prepare an original research work or an advanced project in the field of artificial intelligence technologies, presenting and defending it publicly before a university tribunal with technical rigour, methodological solvency and critical capacity.
The Master’s degree in Artificial Intelligence Technologies is aimed at training highly specialised professionals capable of leading the design, development and application of artificial intelligence solutions in strategic sectors of the economy and society. The programme combines a solid theoretical foundation with a practical approach, providing students with advanced competencies to face current technological challenges and anticipate future ones.
These profiles have been defined in coordination with industry experts and the scientific community, ensuring that the training provided is rigorous, up-to-date and relevant. The combination of advanced technical competencies and cross-cutting skills will enable graduates to adapt to a constantly evolving global labour market and contribute to the technological and social development driven by artificial intelligence.
- Artificial Intelligence Engineer: Specialised in the design, development and implementation of intelligent systems, capable of creating algorithms and models that optimise processes in industrial, service or research environments.
- Machine Learning and Deep Learning Specialist: Prepared for building and optimising machine learning and deep learning models, applying advanced data analysis, prediction and classification techniques on large volumes of information.
- Multi-Agent Systems Developer: Focused on the creation of environments in which multiple autonomous agents interact and cooperate, designing solutions applicable to fields such as automation, collaborative robotics and distributed systems management.
- Researcher in Artificial Intelligence Technologies: With training to contribute to basic and applied research projects, generating innovation in algorithms, architectures and AI applications, both in academia and industry.