- Ability to select and use computational tools to design, implement, and evaluate AI-based applications - Ability to apply innovation management strategies and tools - Ability to communicate technical and scientific results effectively - Ability to assess the technical, ethical, and societal impacts of AI-based solutions

Statistics Data mining Machine learning and deep learning Python language Relational, NOSQL and graph databases

Lectures [30 hours] - Building an artificial intelligence application for both digital and physical environments [1.5h] - Introduction to project pillars: design, innovation management, development and communication [1.5h] - Human-centred design, stakeholder mapping, user personas, functional requirements [1.5h] - Management, WBS, and Gantt [1.5h] - Model-centric and data-centric projects [1.5h] - Foundation models and model hub [1.5h] - Large Language Models and applications (LLMs) [3h] - Transfer Learning and domain adaptation [1.5h] - Retrieval-Augmented Generation (RAG) [1.5h] - Vision Models and applications [3h] - Vision Language Models and applications [3h] - Transfer Learning with Vision Language Models [1.5h] - Multimodal Large Language Models and applications [3h] - AI Ethics and SDGs [1.5h] - Introduction to AI in Physical Environments: Vision-Language-Action Models and Embodied AI [1.5h] - Technical and Scientific Communication [1.5h] Laboratory activities [50h] - Hands-on practical case studies: design and development - Team project development - Team presentation checkpoints

The course is structured in three parts: 1) Introduction to the concepts required to carry out a project 2) Introduction to the tools required to apply these concepts 3) Laboratory sessions for the execution of the projects and meetings with project managers and project leaders from companies, international organisations, and applied research institutes

Copies of the slides used during the lectures will be made available. All teaching material is downloadable from the teaching portal. Reference materials: - Noessel, C. (2017). Designing agentive technology: AI that works for people. Rosenfeld Media. - Ng, A. (2018). Machine learning yearning. DeepLearning.AI. - Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. - Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., et al. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. International Conference on Learning Representations. - Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., et al. (2021). Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning, Proceedings of Machine Learning Research, 139, 8748–8763. - Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474. - Zitkovich, B., Yu, T., Xu, S., Xu, P., Xiao, T., Xia, F., Wu, J., et al. (2023). RT-2: Vision-language-action models transfer web knowledge to robotic control. Proceedings of the 7th Conference on Robot Learning, Proceedings of Machine Learning Research, 229, 2165–2183. - Gemini Robotics Team, Abeyruwan, S., Ainslie, J., Alayrac, J.-B., Arenas, M. G., Armstrong, T., Balakrishna, A., et al. (2025). Gemini Robotics: Bringing AI into the physical world. arXiv:2503.20020. - Nieto-Rodriguez, A. (2021). Harvard Business Review project management handbook: How to launch, lead, and sponsor successful projects. Harvard Business Review Press. - Seely, J. (2013). Oxford guide to effective writing and speaking: How to communicate clearly (3rd ed.). Oxford University Press. - Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic Books. - European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union.

Lecture slides; Text book; Lab exercises;

Exam: Compulsory oral exam; Group project;

The exam includes a mandatory oral part (25% of the overall mark), and the evaluation of the team project assigned during the course (75% of the overall mark). Learning objectives assessment The oral exam covers all the theoretical concepts introduced during the course and will assess - Understanding of computational tools for the design, implementation, and evaluation of AI-based applications - Understanding of innovation management strategies and tools - Understanding of technical and scientific communication principles - Understanding of the technical, ethical, and societal impacts of AI-based solutions The team project and the delivered report will assess - Ability to design, implement and evaluate a complete project - Ability to select and use state-of-the-art computational tools - Ability to use management tools - Ability to document and communicate project results through intermediate and final reports Exam structure and grading criteria The team project consists of managing, designing and implementing an artificial intelligence solution utilising a data science approach. Projects are assigned by companies or applied research institutes. The team project is assigned in the first half of the course. The team project is also characterized by intermediate milestones. Its score is valid for the entire academic year. The evaluation of the team project is based on the performance and accuracy of the proposed solution, in terms of standard quality measures (e.g., prediction accuracy) and completeness (i.e., in-depth analysis of each phase of the designed process and motivation for selecting given techniques and algorithms). The clarity and completeness of the delivered reports will also be considered. The oral exam covers all the theoretical parts of the course. The score is based on the completeness and clarity of the answers. The maximum grade for the team project is 32. The maximum grade for the oral part is 32. The final grade is given by the weighted average of the two parts (0.75*grade team project + 0.25*grade oral part). The exam is passed if the grade of the team project is greater than or equal to 18 and the grade of the oral part is greater than or equal to 18.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.