In order to pass the course, the student will have to carry out all the proposed activities and pass the corresponding exams.

First opportunity:

To pass the subject, the student must deliver and pass the proposed activities (50% of the final grade) and pass the final exam (50% of the grade).

Mid-term exams:

No mid-term exams will be held.

Second opportunity:

The grade obtained in the laboratory practices during the course is maintained, as well as its weight in the final grade. Students who have notreached the cut-off mark in the activities proposed during the previous call, may submit, prior to the second chance final exam, similar activities,which will be proposed by the teachers. Once both parts have been passed separately, the exam will account for the 50% of the final mark and thelaboratory practices for the remaining 50%.

Exemption from attendance:

In case of dispensation of attendance, students will be examined under the same conditions as students in the first round.

Repeating students:

In case of repeating students, they will be examined under the same conditions as students in the first round.

No-show qualification:

The student will receive the qualification of "no-show" when he/she does not take the final exam.

Fraudulent performance of exercises or tests:

The fraudulent execution of tests or assessment activities, once proven, will result in a direct failing grade in the examination in which it was committed. The student will be given a grade of "suspenso" (numeric grade 0) in the corresponding academic year's examination, whether the offense occurs in the first opportunity or the second. In order to do so, the student's grade will be modified in the first opportunity's record, if necessary.

Evaluation of competences:

In general, the development of the practical activities, projects and use cases, as well as the preparation of the theoretical topics will allow students to work on the basic, general and transversal competences of the subject. Specifically, through the projects and use cases, the competences CT7, CT9, CG5, CG4, CG2 will be assessed. The development of the practices, as well as the final test, will allow the evaluation of the specific competences: CE10, CE11, CE12, CE15.

Equality:

According to the various applicable regulations for university teaching, the gender perspective should be incorporated into this subject (using non-sexist language, using bibliographic references from authors of both genders, encouraging the participation of male and female students in class).

Efforts will be made to identify and modify prejudices and sexist attitudes, and the environment will be influenced to change them and promote values of respect and equality.

Situations of gender-based discrimination should be identified, and actions and measures should be proposed to correct them.

Learning outcomes	Study programme competences
To acquire knowledge of how the main incremental learning techniques work.	AC10 AC11 AC12 AC15	BC2 BC3 BC4 BC5 BC6 BC7 BC8 BC9	CC3 CC4 CC7 CC8 CC9
To apply incremental learning techniques for the analysis of real-time data in stationary and non-stationary environments	AC10 AC11 AC12 AC15	BC2 BC3 BC4 BC5 BC6 BC7 BC8 BC9	CC3 CC4 CC7 CC8 CC9
To know the working principle of the main privacy-preserving learning paradigms	AC10 AC11 AC12 AC15	BC2 BC3 BC4 BC5 BC6 BC7 BC8 BC9	CC3 CC4 CC7 CC8 CC9

Topic	Sub-topic
1. Theory	1. Machine Learning Online 2. Concept Drift 3. Federated Learning
2. Practice	1. Machine Learning Online and Concept Drift 2. Federated Learning

Methodologies / tests	Competencies	Ordinary class hours	Student’s personal work hours	Total hours
Guest lecture / keynote speech	A11 A12 A13 A16 B3 B6 B7 B8 B9 C3 C4 C8	10	10	20
Seminar	A11 A12 A13 A16 B2 B4 B5 C7 C9	4	20	24
ICT practicals	A11 A12 A13 A16 B3 B6 B7 B8 C3 C4 C8	7	21	28
Mixed objective/subjective test	A11 A12 A13 A16 B4 B6 B7	1	0	1

Personalized attention		2	0	2

(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies	Description
Guest lecture / keynote speech	The contents of the course will be taught indistinctly between lectures and interactive classes. The completion of all the proposed activities is necessary, as well as the attendance to all the classes (lectures and interactive) to pass the course. Expository classes (theory): will consist of the explanation of the different sections of the course syllabus, with the help of electronic media (presentations, videos, etc.)
Seminar	Case studies: students may be presented with real or fictional work scenarios that present certain problems. Students will have to apply the theoretical and practical knowledge of the subject to find a solution to the question or questions posed. As a general rule, case studies will be carried out in groups. The different working groups will present and share their solutions.
ICT practicals	Interactive classes (practical): different practical problems related to the content of the subject will be posed for the student to solve individually or in groups. Project-based learning: students may be given practical projects whose scope requires them to dedicate a significant part of their time to the subject. Autonomous work: the scope and objectives of the projects, use cases and/or practical problems may require autonomous work on the part of the students, albeit under the supervision of the teaching staff.
Mixed objective/subjective test	A mixed test which can contain quiz questions, short=answer questions or development questions. It is going to evaluate the teorethical part of the subject and it can contain questions about the content of the seminars or practical exercises

Methodologies	Competencies	Description	Qualification
Mixed objective/subjective test	A11 A12 A13 A16 B4 B6 B7	Subjective test which can be a mixture model with quiz questions and some short-answer or development questions.	50
ICT practicals	A11 A12 A13 A16 B3 B6 B7 B8 C3 C4 C8	This mark includes the evaluation exercises made during the practical lessons and the developed project.	30
Seminar	A11 A12 A13 A16 B2 B4 B5 C7 C9	This is going to include the grading og the practical exercises and the proyects developed in the seminars.	20

Assessment comments
In order to pass the course, the student will have to carry out all the proposed activities and pass the corresponding exams. First opportunity: To pass the subject, the student must deliver and pass the proposed activities (50% of the final grade) and pass the final exam (50% of the grade). Mid-term exams: No mid-term exams will be held. Second opportunity: The grade obtained in the laboratory practices during the course is maintained, as well as its weight in the final grade. Students who have notreached the cut-off mark in the activities proposed during the previous call, may submit, prior to the second chance final exam, similar activities,which will be proposed by the teachers. Once both parts have been passed separately, the exam will account for the 50% of the final mark and thelaboratory practices for the remaining 50%. Exemption from attendance: In case of dispensation of attendance, students will be examined under the same conditions as students in the first round. Repeating students: In case of repeating students, they will be examined under the same conditions as students in the first round. No-show qualification: The student will receive the qualification of "no-show" when he/she does not take the final exam. Fraudulent performance of exercises or tests: The fraudulent execution of tests or assessment activities, once proven, will result in a direct failing grade in the examination in which it was committed. The student will be given a grade of "suspenso" (numeric grade 0) in the corresponding academic year's examination, whether the offense occurs in the first opportunity or the second. In order to do so, the student's grade will be modified in the first opportunity's record, if necessary. Evaluation of competences: In general, the development of the practical activities, projects and use cases, as well as the preparation of the theoretical topics will allow students to work on the basic, general and transversal competences of the subject. Specifically, through the projects and use cases, the competences CT7, CT9, CG5, CG4, CG2 will be assessed. The development of the practices, as well as the final test, will allow the evaluation of the specific competences: CE10, CE11, CE12, CE15. Equality: According to the various applicable regulations for university teaching, the gender perspective should be incorporated into this subject (using non-sexist language, using bibliographic references from authors of both genders, encouraging the participation of male and female students in class). Efforts will be made to identify and modify prejudices and sexist attitudes, and the environment will be influenced to change them and promote values of respect and equality. Situations of gender-based discrimination should be identified, and actions and measures should be proposed to correct them.

Basic	Bahri, M., Bifet, A., Gama, J., Gomes, H. M., & Maniu, S (2021). Data stream analysis: Foundations, major tasks and tools. Wiley nterdisciplinary Reviews: Data Mining and Knowledge Discovery,11(3) Orabona, F. (2019). A modern introduction to online learning.. arXivpreprint arXiv:1912.13213 Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation.. CM computing surveys(CSUR),46(4), 1-37 Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions.. IEEE signal processing magazine, 37(3), 50-60 Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications.. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19 Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2018). Learning under concept drift: A review.. IEEE Transactions on Knowledge and DataEngineering,31(12), 2346-2363 Bifet, A., Gavalda, R., Holmes, G., & Pfahringer, B (2018). Machine learning for data streams: with practical examples in MOA. MIT Press Gomes, H. M., Read, J., Bifet, A., Barddal, J. P., & Gama, J. (2019). Machine learning for streaming data: state of the art, challenges, and opportunities.. ACM SIGKDD Explorations Newsletter,21(2), 6-22 Hoi, S. C., Sahoo, D., Lu, J., & Zhao, P. (2021). Online learning: A comprehensive survey. Neurocomputing,459, 249-289.

Complementary	Gama, J., Medas, P., Castillo, G., & Rodrigues, P. (2004). Learning with drift detection. InBrazilian symposium on artificialintelligence(pp. 286-295). Springer, Berlin, Heidelberg. AbdulRahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2020). A survey on federated learning: The journey fromcentralized to distributed on-site learning and beyond.. IEEE Internet of Things Journal, 8(7), 5476-5497 Bifet, A., & Gavalda, R. (2009). Adaptive learning from evolving data streams.. InAdvances in Intelligent Data Analysis VIII McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). Rahman, K. J., Ahmed, F., Akhter, N., Hasan, M., Amin, R., Aziz, K. E., ... & Islam, A. N. (2021). hallenges, applications and design aspects of federated learning: A survey.. IEEE Access,9, 124682-124700. Bifet, A., Gavalda, R. (2007). Learning from time-changing data with adaptive windowing. Proceedings of the 2007 SIAM international conference ondata mining, pp. 443-448. Society for Indust Gama, J., & Castillo, G. (2006). Learning with local drift detection.. Advanced Data Mining and Applications: Second International Conference,ADMA 2006, Xi’an, China, Augu Gomes, H. M., Montiel, J., Mastelini, S. M., Pfahringer, B., & Bifet, A. (2020). On ensemble techniques for data stream regression. In 2020International Joint Conference on Neural Networks (IJCNN) (pp. 1-8) Ghesmoune, M., Lebbah, M., & Azzag, H (2016). State-of-the-art on clustering data streams.. Big Data Analytics, 1, 1-27 (). ttps://federated.withgoogle.com/.