ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 30 Interactive Classroom: 20 Total: 51
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Electronics and Computing
Areas: Computer Science and Artificial Intelligence
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable
The subject deals with some of the most important formal paradigms for the treatment and quantification of uncertainty in reasoning. Methods of graphical representation that simplify the analysis of any probabilistic model will be discussed. The subject shows its applicability with multiple examples from science and engineering. The subsequent introduction of decision theory, in combination with probability theory, makes it possible to choose the optimal alternative from the available information, whether incomplete or ambiguous.
1. Graphical models.
2. Bayesian networks.
3. Exact and approximate inference in graphical models.
4. Sequential models.
5. Markov models.
6. Kalman filters.
7. Decision theory.
8. Decision networks.
9. Game theory.
Basic Bibliography
- S. Russell, P. Norvig. Artificial Intelligence. A Modern Approach. 4th ed. Pearson, 2022.
- K.B. Korb, A.E. Nicholson, Bayesian Artificial Intelligence. 2nd Ed. Chapman&Hall/CRC, 2011.
- Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009.
- M. J. Kochenderfer, T. A. Wheeler, K. H. Wray. Algorithms for Decision Making, MIT Press, 2022.
Complementary Bibliography
- R. Marín, J.T. Palma (Eds.) Inteligencia Artificial y Sistemas Inteligentes. McGraw-Hill, 2008.
- A. Darwiche, Modelling and reasoning with Bayesian networks. Cambridge Univ. Press, 2009.
- Pearl, J., Probabilistic Reasoning in Intelligent Systems: networks of plausible inference. Morgan-Kaufmann, 1988.
- Course on Artificial Intelligence. Fernando Sancho Caparrini. http://www.cs.us.es/~fsancho
Competences
BASICS and GENERAL
- CB4] Students must be able to transmit information, ideas, problems and solutions to both specialised and non-specialised audiences.
- GC2] Ability to solve problems with initiative, decision-making, autonomy and creativity.
- GC4] Ability to select and justify the appropriate methods and techniques to solve a specific problem, or to develop and propose new methods based on artificial intelligence.
SPECIFIC
- CE13] Ability to model and design systems based on knowledge representation and logical or approximate reasoning and apply them to different domains and problems, also in contexts of uncertainty.
CROSS-CUTTING
- TR2] Ability to work in a team, in interdisciplinary environments and managing conflicts.
- TR3] Ability to create new models and solutions autonomously and creatively, adapting to new situations. Initiative and entrepreneurial spirit.
Learning Outcomes
- To know the most important formal paradigms for the treatment and quantification of uncertainty in reasoning.
- Know how to apply graphical models and Bayesian networks, knowing exact and approximate inference.
- To be familiar with probabilistic models when solving problems involving uncertainty.
- Knowledge of decision theory and game theory in problem solving.
The teaching methodology will be based on individual work -although sometimes in groups-, discussion with the teacher in class and individual tutorials.
For each topic or thematic block of the lectures, the teacher will prepare the contents, explain the objectives of the topic to the students in class, suggest bibliography, provide them with additional work material, etc. In the lectures, students will work on the competences CB4, CG2, CG4 and CE13. In addition, the lecturers will propose a set of activities to be carried out individually or in groups (assignments, presentations, readings, practicals, etc.). In general, students will have to present them for assessment, for which the deadlines for delivery/submission will be indicated through the established channels. These activities will allow the development of the above competences and additionally TR2 and TR3.
Students will work individually or in small groups, with the constant support of the teaching staff. Scripts will be available for practicals, seminars and other activities to be carried out individually or in small groups.
Teaching will be supported by the USC virtual platform in the following way: repository of documentation related to the subject (texts, presentations, recommended readings...) and virtual tutoring of students (e-mail, forums).
The learning assessment considers both the theoretical part (60%) and the practical part (40%). In order to pass the subject, an overall mark of 5 or more out of a maximum of 10 points must be obtained, in accordance with the following criteria:
- Theoretical part: this will be assessed in a single exam to be taken on the official date. The grade of the exam must be equal to or higher than 4 out of a maximum of 10 points in order to pass the whole subject. Otherwise, it will have to be repeated at the make-up exam.
- Practical part: evaluation of all the practical activities proposed by the teachers (submission of assignments, presentations in the classroom, submission of exercises, completion of practicals). All the practical activities will have the same weight in the grade for this part. The grade for this part must be equal to or higher than 4 out of a maximum of 10 points in order to pass the whole subject. Submissions with a grade lower than 3 points must be assessed at the second opportunity.
The final grade for the subject will be the arithmetic mean weighted by the percentages indicated above for the theoretical and practical parts. In the event of incurring in any of the situations indicated above due to not achieving the minimum mark required to pass the subject as a whole in one or more parts, the final mark for the opportunity will be the minimum of the marks obtained in those parts.
Students who have not sat the examination or have not submitted to the assessment of any other compulsory activity will be marked as "no-shows".
In order to pass the subject at the second opportunity, students must undergo the assessment of all those compulsory parts pending, in accordance with the above. For the rest, the grades obtained during the course will be retained.
In the case of fraudulent performance of exercises or tests, the provisions of the regulations on the evaluation of the academic performance of students and the review of grades (https://www.xunta.gal/dog/Publicados/2011/20110721/AnuncioG2018-190711-…) will apply. In application of the ETSE regulations on plagiarism (approved by the Xunta da ETSE on 19/12/2019), the total or partial copying of any practical or theory exercise will result in the failure of the two opportunities of the course, with a grade of 0.0 in both cases (https://www.usc.es/etse/files/u1/NormativaPlagioETSE2019.pdf).
Classroom work time: 51 hours in total, divided into 30h (lectures), 20h (seminars and practicals), 1h (tutorials).
Personal work time: 99h (total), divided into 39h (self-study of theory and practice) and 60h (assignments, projects and other activities).
It is recommended that students solve, implement, verify and validate all the proposed exercises and practices (not only the assessable ones). It is also considered important to make intensive use of tutorials to resolve any doubts.
It is recommended to have passed the subjects "Algorithms," "Fundamentals of Machine Learning", "Knowledge Representation and Reasoning" and "Basic Algorithms of Artificial Intelligence".
The teaching language is Spanish and Galician, but in the bibliography and in the supplementary material there may be part of the contents in English.
Alberto Jose Bugarin Diz
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816440
- alberto.bugarin.diz [at] usc.es
- Category
- Professor: University Professor
Jose Maria Alonso Moral
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816432
- josemaria.alonso.moral [at] usc.es
- Category
- Professor: University Lecturer
Ainhoa Vivel Couso
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- ainhoa.vivel.couso [at] usc.es
- Category
- Xunta Pre-doctoral Contract
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| 15:30-17:30 | Grupo /CLIL_01 | Spanish | IA.01 |
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| 05.27.2025 16:00-20:00 | Grupo /CLIL_02 | IA.01 |
| 05.27.2025 16:00-20:00 | Grupo /CLE_01 | IA.01 |
| 05.27.2025 16:00-20:00 | Grupo /CLIL_01 | IA.01 |
| 05.27.2025 16:00-20:00 | Grupo /CLE_01 | IA.11 |
| 05.27.2025 16:00-20:00 | Grupo /CLIL_01 | IA.11 |
| 05.27.2025 16:00-20:00 | Grupo /CLIL_02 | IA.11 |
| 05.27.2025 16:00-20:00 | Grupo /CLE_01 | IA.12 |
| 05.27.2025 16:00-20:00 | Grupo /CLIL_02 | IA.12 |
| 05.27.2025 16:00-20:00 | Grupo /CLIL_01 | IA.12 |
| 07.08.2025 16:00-20:00 | Grupo /CLE_01 | IA.11 |
| 07.08.2025 16:00-20:00 | Grupo /CLIL_02 | IA.11 |
| 07.08.2025 16:00-20:00 | Grupo /CLIL_01 | IA.11 |