ECTS credits ECTS credits: 4.5
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 25 Interactive Classroom: 15 Total: 41
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 objective of this course is to equip students with the ability to identify and appropriately select the most suitable unsupervised learning techniques to solve specific problems within various domains. To achieve this, students must understand the distinctive characteristics of various unsupervised learning techniques, know how to evaluate their strengths and limitations, and determine which technique is most appropriate based on the type of data, its distribution, and the context of the problem to be solved.
Topic 1. Dimensionality Reduction
Topic 2. Clustering Techniques
Topic 3. Frequent Pattern Mining
Topic 4. Anomaly Detection
Topic 5. Data Generation
Basic Bibliography:
- Notes on Unsupervised Machine Learning. Handouts provided by the faculty covering all course topics.
- Hastie, Trevor, Jerome Friedman, and Robert Tibshirani. The Elements of Statistical Learning Data Mining, Inference, and Prediction. New York, NY: Springer New York, 2001. Available online.
- Bishop, Christopher M. Pattern Recognition and Machine Learning. New York: Springer, 2006. Print. Available at ETSE library (C50 721).
Supplementary Bibliography:
- Larose, Daniel T, and Chantal D Larose. Data Mining and Predictive Analytics. Second edition. Hoboken, New Jersey: John Wiley & Sons, 2015. Available online.
- Johnston, Benjamin, Aaron Jones, and Christopher Kruger. Applied Unsupervised Learning with Python: Discover Hidden Patterns and Relationships in Unstructured Data with Python. 1st edition. Birmingham, UK: Packt Publishing, 2019. Available online.
The course contributes to the following competencies:
Basic:
- [CB3] The ability to gather and interpret relevant data (usually within their field of study) to make judgments that include reflection on relevant social, scientific, or ethical issues.
General:
- [CG3] Ability to design and create high-quality models and solutions based on Artificial Intelligence that are efficient, robust, transparent, and responsible.
- [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.
- [CG5] Ability to conceive new computational systems and/or evaluate the performance of existing systems that integrate artificial intelligence models and techniques.
Specific:
- [CE1] Ability to use mathematical and statistical concepts and methods to model and solve artificial intelligence problems.
- [CE12] Understand the foundations of algorithms and models in artificial intelligence to solve complex problems, understand their computational complexity, and have the ability to design new models.
- [CE15] Know and be able to apply and explain correctly the validation techniques for artificial intelligence solutions.
Transversal:
- [TR1] Ability to communicate and transmit their knowledge, skills, and abilities.
- [TR2] Ability to work in a team, in interdisciplinary environments, and manage conflicts.
Learning Outcomes
- Ability to select different unsupervised learning techniques to solve a problem in a specific domain.
- Knowledge and application of clustering techniques based on similarity criteria between data.
- Understanding and application of dimensionality reduction techniques through feature selection and extraction.
The teaching methodology is focused on the theoretical and practical aspects of unsupervised learning and how it differs from other types of learning. Considering this, two types of learning activities are distinguished: theoretical classes and practical classes.
- Theoretical Classes: 25 hours of lectures will be given in 1-hour sessions. These are aimed at explaining the concepts that support this learning paradigm.
- Practical Classes: 15 hours of practical classes will be conducted in the computer lab in 10 sessions of 1.5 hours each, allowing students to familiarize themselves practically with the issues discussed in the theoretical classes. Attendance at these classes is MANDATORY.
Face-to-Face Training Activities and Their Relation to the Degree Competencies
- Theoretical classes given by the professor and seminar presentations. Competencies worked on: CE1, CE12, CE15, CG3, CG4.
- Laboratory practical classes, problem-solving, and practical cases. Competencies worked on: CE1, CE12, CE15, CB3, CG3, CG4, CG5, TR1, TR2.
- Exam. Competencies worked on: CE1, CE12, CE15, CG3, CG4, CG5.
Non-Face-to-Face Training Activities and Their Relation to the Degree Competencies
- Personal work of the student: bibliography consultation, autonomous study, development of programmed activities, preparation of presentations and papers. Competencies worked on: CE1, CE12, CE15, CG3, CG4, CG5.
Regular Assessment Opportunity:
Practical assignments: 40%
Final exam: 60%
Student learning will be assessed through a combination of continuous assessment and a final exam. The course will include the submission of four practical assignments (2.5 points each), aligned with the course content. Each assignment must be submitted on the specified date and in the required format, and will be evaluated through a test. These tests will generally be conducted in the session following the submission.
To pass the course, and provided that the minimum attendance requirements are met, students must obtain a grade equal to or higher than 5 out of 10 in both the practical assignments and the final exam.
Extraordinary Assessment Opportunity:
– Provided that the minimum attendance requirements are met, students may retake the components not passed during the regular assessment (practical assignments and/or final exam) through a recovery exam.
“Not Assessed” Status:
– This status applies to students who have not been assessed in any component of the course. Additionally, students whose participation accounts for no more than 10% of the total maximum grade may also request this status by informing the course coordinator.
Repeating Students:
– Assessment results will not be carried over between academic years.
Attendance Control:
– Attendance at practical sessions is mandatory. If a student attends less than 80% of the practical sessions, the course will be considered failed.
In the case of fraudulent completion of exercises or exams, the Regulations on the Assessment of Academic Performance and the Review of Grades will be applied.
According to the ETSE Regulations on Plagiarism (approved by the ETSE Board on 19/12/2019), total or partial copying of any practical or theoretical exercise will result in failure in both assessment opportunities, with a grade of 0.0 in each case.
Work in the Classroom:
- Theory classes: 25 hours
- Practical classes: 15 hours
- Individual tutoring: 1 hour
Total hours of classroom work: 40 hours
Personal work of students (study, completion of exercises, practical assignments, projects) and other activities (non-classroom evaluation): 71.5 hours
Due to the strong interrelationship between the theoretical and practical parts and the progression in the presentation of closely related concepts in the theoretical part, it is recommended to dedicate daily study or review time.
The USC virtual campus will be used for all teaching, publication of materials, practical scripts, and assignment submissions. The preferred language for delivering expository and interactive classes is Galician/Spanish, but some content in the bibliography and notes may be in English.
Jesus Maria Rodriguez Presedo
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816433
- jesus.presedo [at] usc.es
- Category
- Professor: University Lecturer
Juan Carlos Vidal Aguiar
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816388
- Category
- Professor: University Lecturer
Noel Suárez Barro
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- noel.suarez.barro [at] usc.es
- Category
- Xunta Pre-doctoral Contract
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| 10:00-11:00 | Grupo /CLE_01 | Galician | IA.01 |
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| 12:00-13:30 | Grupo /CLIL_01 | Galician, Spanish | IA.01 |
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| 12:00-13:30 | Grupo /CLIL_02 | Galician, Spanish | IA.02 |
| 06.02.2026 09:15-14:00 | Grupo /CLE_01 | IA.01 |
| 06.02.2026 09:15-14:00 | Grupo /CLIL_01 | IA.01 |
| 06.02.2026 09:15-14:00 | Grupo /CLIL_02 | IA.01 |
| 06.02.2026 09:15-14:00 | Grupo /CLE_01 | IA.02 |
| 06.02.2026 09:15-14:00 | Grupo /CLIL_01 | IA.02 |
| 06.02.2026 09:15-14:00 | Grupo /CLIL_02 | IA.02 |
| 07.06.2026 16:00-20:30 | Grupo /CLIL_01 | IA.01 |
| 07.06.2026 16:00-20:30 | Grupo /CLIL_02 | IA.01 |
| 07.06.2026 16:00-20:30 | Grupo /CLE_01 | IA.01 |