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: First Semester
Teaching: With teaching
Enrolment: Enrollable
In supervised learning, intelligent systems learn by themselves methods for extracting information from data in a supervised manner, i.e. with known values for the quantities to be predicted automatically. Thus, intelligent systems learn to construct functions or models from annotated examples (training data). The learned functions have the capacity of generalization to be applied to new examples from which to make predictions or decisions. This course will teach the most important supervised learning methods and their basic applications (classification, prediction and regression). We will teach how to apply different techniques and choose the most appropriate one for each problem based on its characteristics, data volume and scalability. Some of the most relevant and widely applied strategies with various supervised learning approaches will be discussed. In addition to analyzing the pros and cons of the different approaches studied, some frequent problems that may arise from the training and test data set used, both intrinsic and due to an inappropriate use of the data, will be discussed. Thus, the main objective is to understand the concepts associated with machine learning from previously labeled data, as well as its mathematical foundations.
Unit 1. Preliminary concepts of machine learning:
1.1 Classification and linear regression.
1.2 Metrics and model selection.
Unit 2. Interpretable supervised machine learning
2.1 Nearest neighbor methods.
2.2 Learning Rule-based systems.
2.3 Decision trees.
Unit 3. Non-nterpretable supervised machine learning
3.1 Ensembles: bagging, boosting and random forest.
3.2 Support Vector Machines.
3.3 Neural Networks
1) Basic bibliography:
Notes of supervised machine learning. Notes will be provided, in the form of PDF presentations prepared by the teaching staff, covering all the topics of the syllabus
G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning with Applications in R. Ed. 1, New York: Springer, 2013. ISBN 978-1-4614-7137-0.
T. Hastie, R. Tibshirani, J. Friedman. The elements of statistical learning: data mining, inference, and prediction. Ed. 2, New York: Springer, 2009. ISBN 978-0-3878-4857-0.
2) Complementary bibliography:
C.M. Bishop. Pattern recognition and machine learning, Springer, 2006, ISBN: 978-0387310732
I.H. Witten, E. Frank, M.A. Hall, C.J. Pal, DATA MINNIG. Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems). 4th Edition. ISBN-10:0128042915, ISBN-13:978-0128042915
P. Harrington. Machine learning in action. O'Reilly, 2012. ISBN 978-1617290183
A. Géron. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc., ISBN:9781492032649, 2nd Edition
J. Hurwitz, D. Kirsch. Machine learning for dummies. John Wiley & Sons, Inc., 2018. ISBN 9781119454953
Throughout the course, students will develop the following skills:
BASIC:
CB2 - That students know how to apply their knowledge to their work or vocation in a professional manner and possess the competencies usually demonstrated through the development and defense of arguments and problem solving within their area of study.
GENERAL:
CG2.Ability to solve problems with initiative, decision-making, autonomy and creativity.
CG4. 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.
GC5. Ability to design new computational systems and/or evaluate the performance of existing systems that integrate artificial intelligence models and techniques.
TRANSVERSAL:
TR1 - Ability to communicate and transmit their knowledge, skills and abilities.
TR2 - Ability to work in a team, in interdisciplinary environments and managing conflicts.
SPECIFIC:
CE1 - Ability to use mathematical and statistical concepts and methods to model and solve artificial intelligence problems.
CE12. Know the basics of algorithms and models of artificial intelligence for solving problems of certain complexity, understand their computational complexity and have the ability to design new models.
CE15. Know and know how to apply and explain correctly the validation techniques of artificial intelligence solutions.
Accordingly, the Learning Outcomes will be:
+ Know how to select the different supervised learning techniques to solve a problem in a certain domain.
+ Know the most important formal paradigms for supervised learning from data.
+ Know supervised learning techniques for classification and regression tasks.
+ Know the advantages and disadvantages of different supervised learning strategies, models and combination of models, but also evaluation metrics and validation methodologies to select the best models.
+ Know how to apply the algorithms and models studied in various use cases.
Lectures (30 h): master class presentations to explain the contents of the different topics, with special emphasis on the explanation and assimilation of concepts, mathematical foundations and the potential usefulness of supervised machine learning.
Interactive classes (20 h): resolution of practical classification and regression problems.
The course is 100% face-to-face.
Final exam with multiple-choice and short-answer questions on the contents covered in the lectures: 60% of the final grade.
Continuous evaluation: evaluation of the deliverables and results obtained in the different interactive sessions and exercises of the course: 40% of the final grade.
The attendance and participation of the students in the lectures as well as in the practicals, debates and/or seminars carried out during the course is encouraged.
After each assignment associated with the practical classes, there will be an assessment test. The test will take place in the lecture coming next after the submission milestone and attendance will be required. Attendance will be monitored through sign-in sheets, which must be completed at the end of each session.
The grade for each practical assignment will consist of two components: 70% for the evaluation of the deliverables (based on the correctness and clarity of the developed code and the associated report) and 30% for the grade achieved on the assessment test associated with the assignment.
These assessment activities will be mandatory and will be carried out in a lecture (i.e., a theory session), so, for the purposes of what is established in Art. 1 of the Regulations for class attendance in official undergraduate and master's degrees at the University of Santiago de Compostela (25/11/2024) ”, attendance at the sessions where these activities are scheduled will be mandatory, being a requirement to carry them out that, if not fulfilled, will result in a grade of 0.0 in the corresponding practical assignment. Except as indicated in this point, class attendance will not have any other effect in the evaluation system, although attendance at all teaching activities is recommended since it contributes to improving the understanding of the subject and facilitating the acquisition of skills.
In order to pass the course it is necessary to pass both the final exam and the continuous evaluation.
The delivery of one of the reports associated to practical assignments (or any other evaluation item) will mean that the student has chosen to take the course. Therefore, from that moment on, since he/she has not yet taken the final exam, one opportunity will be considered to have been used up.
In the second opportunity evaluation (July), the internship grade will be maintained if the student has a grade of 5 or more points. Otherwise, the student will have to be evaluated again for the practical part of the subject in this second opportunity. In any case, the student will have to take the final exam and the previous grade will not be retained. From then on, the same criteria already established for the first opportunity will be applied to pass the course.
Repeating students: Evaluations between courses will not be retained.
In case of fraudulent examinations, the “Regulations for the evaluation of students' academic performance and grade review” (https://www.xunta.gal/dog/Publicados/2011/20110721/AnuncioG2018-190711-…) will be applied. Total or partial copying of any practice or theory exercise will automatically result in a grade of 0.0 in the subject and opportunity.
Classroom work time: 50h (total), divided into 30h (lectures), 20h (interactive classes).
Personal work time: 100h (total), including study, exercises and other evaluation activities.
It is recommended that students attend class and solve, verify and validate all the proposed exercises and practices (not only the evaluable ones), using the machine learning libraries used in the course and through direct programming.
Complementary means of teaching: virtual course on the platform provided by the USC, developed and constantly updated by the teachers of the subject.
The predominant teaching language is Spanish, but both in the bibliography and in the supplementary material there may be part of the contents in English.
Manuel Felipe Mucientes Molina
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816434
- manuel.mucientes [at] usc.es
- Category
- Professor: University Professor
Jose Maria Alonso Moral
Coordinador/a- 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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| 10:30-12:00 | Grupo /CLE_01 | Spanish | IA.01 |
| 12:00-14:00 | Grupo /CLIL_01 | Spanish | IA.02 |
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| 12:00-14:00 | Grupo /CLIL_02 | Spanish | IA.02 |
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| 09:00-10:30 | Grupo /CLE_01 | Spanish | IA.01 |
| 01.20.2026 16:00-20:00 | Grupo /CLE_01 | IA.01 |
| 01.20.2026 16:00-20:00 | Grupo /CLIL_01 | IA.01 |
| 01.20.2026 16:00-20:00 | Grupo /CLIL_02 | IA.01 |
| 01.20.2026 16:00-20:00 | Grupo /CLIL_01 | IA.02 |
| 01.20.2026 16:00-20:00 | Grupo /CLIL_02 | IA.02 |
| 01.20.2026 16:00-20:00 | Grupo /CLE_01 | IA.02 |
| 06.25.2026 09:30-14:00 | Grupo /CLE_01 | IA.11 |
| 06.25.2026 09:30-14:00 | Grupo /CLIL_01 | IA.11 |
| 06.25.2026 09:30-14:00 | Grupo /CLIL_02 | IA.11 |