ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Hours of tutorials: 4 Expository Class: 20 Interactive Classroom: 24 Total: 48
Use languages Spanish, Galician
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Electronics and Computing
Areas: Computer Science and Artificial Intelligence
Center Faculty of Sciences
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The main learning outcomes of this subject:
- Identify and know the different AI techniques oriented to machine learning (i.e., Machine Learning).
- Identify the different AI techniques oriented to deep learning (i.e., Deep Learning).
- To know how to apply the most appropriate machine learning or deep learning model for a given data analysis/exploitation problem in the biomolecular field.
- To know how to use software platforms to build models based on automatic or deep learning from biomolecular data.
- To know how to preprocess, interpret and reduce the dimensionality of large-scale biomolecular data to facilitate their treatment by automatic or deep learning.
Theory:
Introduction to machine learning.
Supervised and unsupervised learning (i.e., Decision Trees, Gradient Boosting, Support Vector
Machine, etc).
Reinforcement learning.
Introduction to Federated Learning.
Introduction to deep learning.
Convolutional neural networks (CNN).
Combination and selection of models.
Platforms and software tools for machine and deep learning.
Ethical aspects derived from the application of Artificial Intelligence techniques.
Practices: practical cases in the laboratory on the different theoretical contents of the subject.
Seminars:
Carrying out and exposing works on the different theoretical contents of the subject.
Practices:
Practical cases in the laboratory on the different theoretical contents of the subject.
Basic bibliography:
D. Borrajo, J. González y P. Isasi. Aprendizaje automático. Sanz y Torres.
T.M. Mitchell. Machine Learning. McGraw Hill.
Complementary bibliography:
E. Rich y K. Knight. Artificial Intelligence. McGraw-Hill.
S. Russel y P. Norving. Artificial Intelligence: a modern approach. Prentice Hall. 2003
Comp05 - Collaborate in interdisciplinary teams in any work environment, based on knowledge of the environment and current legal regulations
Comp07 - That students have the ability to gather and interpret relevant data (normally within their area of ​​study) to make judgments that include reflection on relevant issues of a social, scientific or ethical nature.
Con08 - Identify and learn about the different AI techniques aimed at machine learning and deep learning to build models from field data
H/D10 - Reprocess, interpret and reduce the dimensionality of large-scale biomolecular data, to facilitate its treatment through machine or deep learning
For the correct acquisition of the skills by the student, the following general set of methodologies has been adopted for the degree:
1st) Expository teaching
2nd) Interactive teaching:
Practices (in the laboratory and/or in the computer room)
Problem solving and/or practical cases
Seminars
Tutorials in small groups
ÆæȤÌÚѶ·Ö·Ö²Ê Virtual Campus
3rd) Carrying out work:
Individual works, without or with exhibition
Group work, without or with exhibition
4th) Individual tutorials
Given that, based on this Report and the general regulations of the ÆæȤÌÚѶ·Ö·Ö²Ê, the distribution of face-to-face hours of expository teaching, practices, seminars and group tutorials, as well as individual tutorials, is already established for each subject and when appropriate,
The teacher responsible for teaching each subject will establish and state in his teaching guide which methodologies he will use among those previously mentioned.
• Final exam (value between 10%-40% of the overall grade - Comp07, H/D10
• Continuous monitoring (5%-35%) -Comp05
• Assessment of practical work (10%-40%) - Comp07, Comp05, H/D10, Con08
• Evaluation of tutored work (10%-40%) - Comp05, Con08
For cases of fraudulent completion of exercises or tests, the application established in the "Regulations for the evaluation of the academic performance of students and the revision of qualifications" will be applied".
Theory classes/Master class – 24 face-to-face hours + 48 non-face-to-face hours
Practical laboratory classes/Laboratory practices – 12 face-to-face hours + 20 non-face-to-face hours
Carrying out supervised work/Autonomous work – 0 face-to-face hours + Z non-face-to-face hours
None
None
Sonia Maria Valladares Rodriguez
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- sonia.valladares [at] usc.es
- Category
- PROFESOR/A PERMANENTE LABORAL
| Tuesday | |||
|---|---|---|---|
| 11:00-12:00 | Grupo /CLE_01 | Spanish | 1P CLASSROOM 1 FIRST FLOOR |
| Wednesday | |||
| 10:00-12:00 | Grupo /CLE_01 | Spanish | 1P CLASSROOM 1 FIRST FLOOR |
| Thursday | |||
| 10:00-12:00 | Grupo /CLE_01 | Spanish | 1P CLASSROOM 1 FIRST FLOOR |
| 05.13.2025 10:00-13:00 | Grupo /CLE_01 | 1P CLASSROOM 1 FIRST FLOOR |
| 06.30.2025 10:00-13:00 | Grupo /CLE_01 | 1P CLASSROOM 1 FIRST FLOOR |