Study programme competencies |
Code
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Study programme competences / results
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A1 |
Capacidad para utilizar los conceptos y métodos matemáticos y estadísticos para modelizar y resolver problemas de inteligencia artificial. |
A2 |
Capacidad para resolver problemas de inteligencia artificial que precisen algoritmos, aplicando correctamente metodologías de desarrollo software y diseño centrado en usuario/a. |
A12 |
Conocer los fundamentos de los algoritmos de la inteligencia artificial y la optimización, entender su complejidad computacional y saber aplicarlos a la resolución de problemas. |
A15 |
Conocer y saber aplicar y explicar correctamente las técnicas de validación de las soluciones de inteligencia artificial. |
B3 |
Que el alumnado tenga la capacidad de reunir e interpretar datos relevantes (normalmente dentro de su área de estudio) para emitir juicios que incluyan una reflexión sobre temas relevantes de índole social, científica o ética. |
B5 |
Que el alumnado haya desarrollado aquellas habilidades de aprendizaje necesarias para emprender estudios posteriores con un alto grado de autonomía. |
B7 |
Capacidad para resolver problemas con iniciativa, toma de decisiones, autonomía y creatividad. |
B9 |
Capacidad para seleccionar y justificar los métodos y técnicas adecuadas para resolver un problema concreto, o para desarrollar y proponer nuevos métodos basados en inteligencia artificial. |
B10 |
Capacidad para concebir nuevos sistemas computacionales y/o evaluar el rendimiento de sistemas existentes, que integren modelos y técnicas de inteligencia artificial. |
C3 |
Capacidad para crear nuevos modelos y soluciones de forma autónoma y creativa, adaptándose a nuevas situaciones. Iniciativa y espíritu emprendedor. |
Learning aims |
Learning outcomes |
Study programme competences / results |
Know, understand and know how to use the fundamentals of machine learning processes. |
A1 A12
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B5 B10
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C3
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Know the fundamentals of regression, classification and clustering models. |
A2 A12
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B3 B7 B9
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Know how to build advanced statistical models for data analysis. |
A2 A12 A15
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B7 B9
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C3
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Know how to base modeling and problem solving using machine learning techniques. |
A1 A2 A12
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B5 B7 B9
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C3
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Know how to build machine learning models for regression, classification and clustering. |
A1 A2 A12 A15
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B5 B7 B9 B10
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C3
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Contents |
Topic |
Sub-topic |
1. Introduction |
1.1. Introduction to Machine Learning
1.2. Learning Paradigms
1.3. Inductive Learning
1.4. No free Lunch Theorems
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2. Supervised learning |
2.1. Introduction
2.2. Artificial Neural Networks
2.3. Support Vector Machines
2.4. Decision trees
2.5. Regression trees and regression model trees
2.6. Instance-based learning
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4. Methodologies in data analysis |
4.1. Training, evaluation and model selection methodologies
4.2. Methodologies of a data analysis project
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3. Evolutionary Computation |
3.1. Genetic Algorithms
3.2. Genetic Programming
3.3. Swarms and other Evolutionary Computation techniques
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5. Clustering |
5.1. Clustering methods |
Planning |
Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
Guest lecture / keynote speech |
A12 A15 B5 B9 B10 C3 |
30 |
38 |
68 |
Laboratory practice |
A1 A2 B3 B7 C3 |
15 |
24 |
39 |
Supervised projects |
A1 A2 A15 B3 B7 B10 |
15 |
24 |
39 |
Objective test |
A1 A12 B5 B7 B10 |
2 |
0 |
2 |
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Personalized attention |
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2 |
0 |
2 |
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(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Methodologies |
Description |
Guest lecture / keynote speech |
Theoretical teaching of the subject matter of the course |
Laboratory practice |
Solve practical problems by using the different techniques that will be explained in the theory classes. |
Supervised projects |
Writing, under the supervision of the teacher, of the reports explaining the resolution of the problems carried out in the laboratory practices and the results obtained. |
Objective test |
This is a written assessment test in which the student must demonstrate the knowledge acquired from the subject. |
Personalized attention |
Methodologies
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Laboratory practice |
Supervised projects |
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Description |
Practical work carried out with the advice of the teacher.
Writing of the explanatory report under the teacher's supervision.
Office hours: Office hours will be used to solve students' doubts related to the contents of the subject. These office hours can be both face-to-face and virtual (via email, virtual campus or Microsoft Teams platform).
Virtual Classroom: This subject will have a virtual classroom where students will be provided with all the necessary material in digital format. Different communication tools will also be provided to support both teaching and office hours, including videoconferencing, chat, e-mail, forums... |
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Assessment |
Methodologies
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Competencies / Results |
Description
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Qualification
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Laboratory practice |
A1 A2 B3 B7 C3 |
Development of a Machine Learning system based on explanations made in theory. |
25 |
Supervised projects |
A1 A2 A15 B3 B7 B10 |
Writing of the report on the resolution of the real problem carried out in the laboratory practices. The writing of the report will include a bibliographic review of the most important works related, written in English for the most part, documentation on the problem to be solved, methodology used, and comparison of the results found in the application of the different techniques, as well as a critical evaluation of both the results obtained and the information used. |
25 |
Objective test |
A1 A12 B5 B7 B10 |
Test questions about the contents of the course, based on the different machine learning techniques and their applications. |
50 |
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Assessment comments |
In order to pass the subject, the student must obtain a minimumscore of 5 out of 10 in the result of combining the grades of the objectivetest, the laboratory practices and the supervised works. In addition, thestudent must obtain a minimum score of 2 out of 5 points in the objective test.If the student does not obtain this minimum grade, the grade of the subjectwill be that corresponding to the grade of the objective test. In the second opportunity, the grade obtained in the laboratory practices and supervised works will be maintained, not being able to obtain again a grade since it results from the continuous evaluation of the workduring the credits of practice of the subject. The student can retake the examination of the objective test, the criteria for obtaining the total scorebeing those indicated at the beginning of this section. Part-time students must deliver in their reports on the same dates as full-time students, and attend the RGTs in which they will be corrected. Similarly, it is recommended that they attend the practice classes. 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.
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Sources of information |
Basic
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D. Borrajo, J. González, P. Isasi (2006). Aprendizaje automático. Sanz y Torres
Basilio Sierra Araujo (2006). Aprendizaje automático: conceptos básicos y avanzados. Aspectos prácticos utilizando el software WEKA. Pearson Education
Marcos Gestal, Daniel Rivero, Juan Ramón Rabuñal, Julián Dorado, Alejandro Pazos (2010). Introducción a los Algoritmos Genéticos y a la Programación Genética. Servicio de Publicaciones de la Universidade da Coruña
Ethem Alpaydin (2004). Introduction to Machine Learning. MIT Press
David Aha (). Lazy Learning. Kluwer Academics Publishers
T.M. Mitchell (1997). Machine Learning. McGraw Hill
Saso Dzeroski, Nada Lavrac (). Relational Data Mining. Springer
Andrew Webb (2002). Statistical Pattern Recognition. Wiley |
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Complementary
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Recommendations |
Subjects that it is recommended to have taken before |
Programming I/614G03006 | Programming II/614G03007 | Discrete Mathematics/614G03003 | Algebra/614G03001 | Calculus and Numerical Analysis/614G03002 | Statistics/614G03004 | Algorithms/614G03008 | Basic Algorithms of Artificial Intelligence/614G03019 | Knowledge Representation and Reasoning/614G03020 |
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Subjects that are recommended to be taken simultaneously |
Mathematical Optimisation/614G03005 |
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Subjects that continue the syllabus |
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