Masters Applied Artificial Intelligence and Computation

The creation of this master's program is part of a set of actions planned in the context of the integration of Lusófona University as a full member of the EIT's DIGITAL KIC. The opportunities created by integration into international projects and networks, and the mastery of English in academic and professional contexts in the areas in which the proposal is included, justify the presentation of this Study Cycle for teaching in Portuguese and English. This master's program is the result of extensive discussions with eight other European universities. The selection of course units (CUs) resulted from consensus among all partners. An important aspect of this consensus was the fact that, despite the many new developments and important paradigms in AI, all of this stems from seventy years of fundamental AI developments. We believe that this master's program should balance fundamental knowledge and emerging paradigms. This means that it is possible to include new CUs over time (as options). These CUs may include what best reflects emerging paradigms in the field of AI (referring mainly to CUs covering topics such as generative AI, specifically training, fine-tuning and prompting systems, such as Large Language Models (LLMs) and others). Since the master's program aims to include the diverse technical backgrounds of students, we do not assume prior knowledge of AI. To accommodate the diversity of candidates' knowledge, we have created a range of optional course units. Students with prior experience in AI can choose more advanced course units, while students with less experience can enroll in fundamental course units to develop the skills needed for more advanced mandatory course units.

The following may apply for the master's degree program: a) Holders of a bachelor's degree or equivalent; b) Holders of a foreign academic degree awarded following a first cycle of studies organized according to the principles of the Bologna Process by a State adhering to that process; c) Holders of a foreign higher academic degree that is recognized as satisfying the objectives of a bachelor's degree by the statutorily competent body; d) Holders of a school, scientific or professional curriculum that is recognized by the statutorily competent body of the Lusófona University as attesting to the capacity to undertake this cycle of studies.

The primary target audience for the Master's in Applied Artificial Intelligence and Computing, focused on practical applications, are first and foremost professionals who already have a solid technical foundation and wish to transition to or accelerate their careers in cutting-edge AI/ML (e.g., Software Engineers/Programmers, Data Scientists, Electrical, Mechanical, or Robotics Engineers). But not only for these. This Master's program is also for Product Managers and Technical Leaders in Technology (professionals who lead teams or products where AI is a critical component) and Business/Technology Consultants in Consulting Firms (to advise clients on digital transformation and AI implementation, they need a technical understanding that goes beyond the superficial), and of course for Recent Graduates with an Excellent Foundation in technology or science (students who have completed a degree in Data Science, Computer Science, Mathematics, Physics or related fields and who, instead of following an academic/purely theoretical path, want to immediately start a highly practical and well-paid career as AI engineers).

The Master's program in Applied Artificial Intelligence and Computing ensures that students acquire knowledge in the theoretical, methodological, and practical foundations of AI, encompassing machine learning, natural language processing, computer vision, and data analysis. Proficiency is developed in programming languages, primarily Python, for the creation and implementation of AI models. Cloud platforms are used for scalable AI applications. Ethical considerations are emphasized in accessing and processing vast datasets in compliance with data protection laws. Students should demonstrate advanced skills in managing complex AI infrastructures, analyzing data to obtain actionable insights, and applying cutting-edge techniques for experimental analysis. The program aims to develop analytical autonomy, enabling students to solve non-trivial problems independently and collaborate effectively in multidisciplinary teams.