Base Knowledge
Concepts learned in the Machine Learning course unit will be used.
Teaching Methodologies
Theoretical classes are expository but they depend on the students’ participation, particularly through interactive questions in some lessons.
Practical classes are based on following the development of the practical work interleaved with solving exercises. Some classes will be exclusively dedicated to solving exercises.
All elements of support for theoretical-practical classes are made available to students.
Learning Results
It is intended that students acquire a set of knowledge and skills in the area of Ambient Intelligence, namely:
- Know and understand the concepts and technologies
- Know, understand and apply data acquisition and fusion techniques from different sensors
- Know and understand the different dimensions of context, in particular the spatial dimension, through the manipulation, visualisation and analysis of georeferenced data
- Select and apply appropriate machine learning techniques to the data collected to infer patterns about the context and its dimensions
- Enable the implementation of intelligent agents capable of interpreting data, reasoning about contextual information, and acting proactively
- Know and promote privacy in the acquisition, protection and treatment of the data collected
Program
Theoretical component:
1. Introduction
- Comparison and definition of AmI and Ubiquitous Computing
- Mark Weiser’s vision. The ISTAG vision
- Fundamental concepts of AmI
2. Location-based systems
- Spatial Databases
- Geographic Information Systems
- Geospatial data analysis
3. Sensors, Actuators and Modelling
- Opportunistic sensors and private sensors
- Internet of Things (IoT)
- Context-Aware Computing
- Context-Aware Computing for IoT
4. Artificial Intelligence & AmI
- Intelligent spatial algorithms and data structures that use context: Map Matching, Routing, Voronoi diagrams
- Data fusion and clustering
- Contextual data classification
- Agentic AI
- Intelligence of Things
5. Privacy
- Security vs. Privacy
- General Data Protection Regulation
- Technical solutions
6. User experience at AmI
- Human-Computer Interaction and adaptability
- Intelligent interfaces
- Field studies
7. Ambient Intelligence applications
- Intelligent Transport Systems
- Smart cities and Urban Computing
- Smart Environments
- Precision Agriculture
- Industry 4.0
Practical component:
1. Data collection through mobile devices
- Different context sensors (location, movement, orientation, temporal, environmental)
- Open data platforms
- Contextual open data available
2. Storage, visualisation and application of spatial algorithms in contextual data
- Spatial Databases
- Geographic Information System
- Mapping, routing and creating Voronoi diagrams Data
- Anonymisation Techniques
3. Artificial Intelligence & AmI
- Pre-processing and data fusion for context-awareness machine learning
- Exploratory Data Analysis (EDA)
- Classification and Clustering
- Agentic AI
Curricular Unit Teachers
Ana Cristina Costa Oliveira AlvesGrading Methods
Periodic assessment where the final grade of the course unit is obtained through the following expression, considering each component graded in the range 0 to 100%:
Final grade = 0.5 * Exam + 0.5 * Practical work
Thus, in a range from 0 to 20 values, the components have the following weights:
- Exam: 10 values
- Practical work: 10 values
Exam
Written exam, with conditioned and physical consultation (non-digital), which will focus on the contents taught in the theoretical and practical classes.
It is possible to replace the interactive questions presented in some lessons with an exam question. Performance on these questions will be announced in good time before the start of the normal exam period.
It is mandatory to obtain a rating equal to or greater than 25%. Failure to obtain this minimum classification implies the failure of the discipline.
No informal grading process is allowed between exam periods.
The exam will be carried out in the periods destined for the exam periods (Normal, 2nd Call, Special).
Practical Work
Practical work on the free theme must be previously approved or proposed by the teacher. The choice to take the theme proposed by the teacher implies a minimum attendance of 75% in practical classes.
Students should preferably form groups of 2 students. However, individual works are allowed.
There are 3 profiles for the theme of practical work: (1)Data Analysis, (2) Software Development or (3) Self-proposed (validated free theme).
This course has 3 elements of evaluation:
- a) Proposal Presentation in a theoretical class (for self-proposed themes) - 0.75 value (not for those who choose the proposed theme by the teacher).
- b) Research, sharing and written synthesis of the work-related in a descriptive document following a given template of the application/analysis to be developed - 3 values (2.25 for those who choose a self-proposed theme).
- c) Final presentation with demonstration and submission of the final version of the descriptive document - 7 values (the same weight and mandatory for all).
The work is indivisible: all elements are mandatory to obtain the final grade.
After the b) assessment moment of the practical work, students should be prepared to answer questions about the work and options for the next class.
All delivery/presentation dates will be announced/confirmed later.
The work will only be carried out in groups of 2 students during the normal season/resource. In the special season, the work is done individually as not all students are eligible to access that season. The grades awarded may differ between the members of each group.
As soon as delivered, the grade of the practical work will be considered in all subsequent exam periods to be carried out during the school year (Normal, 2nd call, Special). As such, there will be no new opportunity to improve the elements of practical assessment.
Each assessment element may be subject to an oral examination.
For working students or students with any special status provided for by law, the provisions of the law apply. The situation must be confirmed from the information provided by the Academic Services, until the last day of classes.
Internship(s)
NAO
Bibliography
Mandatory:
1] Augusto, J., Callaghan, V., Cook, D., Kameas, A., Satoh, I. (2013). Intelligent Environments: a Manifesto. Human-centric Computing and Information Sciences, 3:12 (https://doi.org/10.1186/2192-1962-3-12 )
[2] Hernández-Torres, GA., Sánchez-DelaCruz, E. Challenges and opportunities of ambient intelligence (AmI) in the 21st century: a historical review. Evolutionary Intelligence. 18, 80 (2025). https://doi.org/10.1007/s12065-025-01067-1
[3] Bimpas, A., Violos, J., Leivadeas, A., & Varlamis, I. (2024). Leveraging pervasive computing for ambient intelligence: A survey on recent advancements, applications and open challenges. Computer Networks, 239, 110156. https://doi.org/10.1016/J.COMNET.2023.110156
[4] Chin, J., Callaghan, V., & Allouch, S. B. (2019). The Internet-of-Things: Reflections on the past, present and future from a user-centered and smart environment perspective. Journal of Ambient Intelligence and Smart Environments, 11(1), 45-69 (https://doi.org/10.3233/AIS-180506)
[5] Gams, M., Gu, I. Y. H., Härmä, A., Muñoz, A., & Tam, V. (2019). Artificial intelligence and ambient intelligence. Journal of Ambient Intelligence and Smart Environments, 11(1), 71-86 (https://doi.org/10.3233/AIS-180508)
[6] Tissaoui, A. (2026). From prompt to persona: a literature review on LLMs as single cognitive agents. Journal of Ambient Intelligence and Humanized Computing 17, 205–221. https://doi.org/10.1007/s12652-025-05029-4
Optional:
1] Augusto, J., Callaghan, V., Cook, D., Kameas, A., Satoh, I. (2013). Intelligent Environments: a Manifesto. Human-centric Computing and Information Sciences, 3:12 (https://doi.org/10.1186/2192-1962-3-12 )
[2] Hernández-Torres, GA., Sánchez-DelaCruz, E. Challenges and opportunities of ambient intelligence (AmI) in the 21st century: a historical review. Evolutionary Intelligence. 18, 80 (2025). https://doi.org/10.1007/s12065-025-01067-1
[3] Bimpas, A., Violos, J., Leivadeas, A., & Varlamis, I. (2024). Leveraging pervasive computing for ambient intelligence: A survey on recent advancements, applications and open challenges. Computer Networks, 239, 110156. https://doi.org/10.1016/J.COMNET.2023.110156
[4] Chin, J., Callaghan, V., & Allouch, S. B. (2019). The Internet-of-Things: Reflections on the past, present and future from a user-centered and smart environment perspective. Journal of Ambient Intelligence and Smart Environments, 11(1), 45-69 (https://doi.org/10.3233/AIS-180506)
[5] Gams, M., Gu, I. Y. H., Härmä, A., Muñoz, A., & Tam, V. (2019). Artificial intelligence and ambient intelligence. Journal of Ambient Intelligence and Smart Environments, 11(1), 71-86 (https://doi.org/10.3233/AIS-180508)
[6] Tissaoui, A. (2026). From prompt to persona: a literature review on LLMs as single cognitive agents. Journal of Ambient Intelligence and Humanized Computing 17, 205–221. https://doi.org/10.1007/s12652-025-05029-4
Opcional:
[7] Dunne, R., Morris, T., & Harper, S. (2021). A Survey of Ambient Intelligence. ACM Computing Surveys, 54, 4, Article 73 (May 2022), 27 pages (https://doi.org/10.1145/3447242)
[8] Elazhary, H. (2019). Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. Journal of Network and Computer Applications, 128, 105-140 (https://doi.org/10.1016/j.jnca.2018.10.021).
[9] Ubiquitous Computing Fundamentals. (2010). Edited by John Krumm, ISBN: 978-1420093605, CRC Press. [1A-21-1 (ISEC) – 18970]
[10] Müller, A., C., & Guido, S. (2017). Introduction to machine learning with Python : a guide for data scientists. ISBN 978-1-449-36941-5, O’Reilly. [ 1A-4-197 (ISEC) – 18236]
Other relevant papers (available online via b-on)