| Study programme competencies |
| Code | Study programme competences |
| A11 | CE10 - Ability to implement, validate and apply a stochastic model starting from the observed data on a real system, and to perform a critical analysis of the obtained results, selecting those ones most suitable for problem solving |
| A12 | CE11 - Understanding and command of the main techniques and tools for data analysis, both from the statistical and the machine learning viewpoints, including those devised for large volumes of data, and ability to select those ones most suitable for problem solving |
| A13 | CE12 - Ability to outline, formulate and solve all the stages of a data project, including the understanding and command of basic concepts and techniques for information search and filtering in big collections of data |
| A16 | CE15 - Knowledge of computer tools in the field of machine learning and ability to select those ones most suitable for problem solving |
| B2 | CG02 - Successfully addressing each and every stage of an AI project |
| B3 | CG03 - Searching and selecting that useful information required to solve complex problems, with a confident handling of bibliographical sources in the field |
| B4 | CG04 - Suitably elaborating written essays or motivated arguments, including some point of originality, writing plans, work projects, scientific papers and formulating reasonable hypotheses in the field |
| B5 | CG05 - Working in teams, especially of multidisciplinary nature, and being skilled in the management of time, people and decision making |
| B6 | CB01 - Acquiring and understanding knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, frequently in a research context |
| B7 | CB02 - The students will be able to apply the acquired knowledge and to use their capacity of solving problems in new or poorly explored environments inside wider (or multidisciplinary) contexts related to their field of study |
| B8 | CB03 - The students will be able to integrate different pieces of knowledge, to face the complexity of formulating opinions (from information that may be incomplete or limited) and to include considerations about social and ethical responsibilities linked to the application of their knowledge and opinions |
| B9 | CB04 - The students will be able to communicate their conclusions, their premises and their ultimate justifications, both to specialised and non-specialised audiences, using a clear style language, free from ambiguities |
| C3 | CT03 - Use of the basic tools of Information and Communications Technology (ICT) required for the student's professional practice and learning along her life |
| C4 | CT04 - Acquiring a personal development for practicing a citizenship under observation of the democratic culture, the human rights and the gender perspective |
| C7 | CT07 - Developing the ability to work in interdisciplinary or cross-disciplinary teams to provide proposal that contribute to a sustainable environmental, economic, political and social development |
| C8 | CT08 - Appreciating the importance of research, innovation and technological development in the socioeconomic and cultural progress of society |
| C9 | CT09 - Being able to manage time and resources: outlining plans, prioritising activities, identifying criticisms, fixing deadlines and sticking to them |
| Learning aims | |||
| Learning outcomes | Study programme competences | ||
| Ability to identify if a problem can be solved using a machine learning technique. | AC12 |
BC2 BC3 BC4 BC8 |
CC4 CC7 CC8 CC9 |
| Obtain the ability to choose the most appropriate learning technique for a problem depending on the nature of the data. | AC11 AC15 |
BC2 BC6 BC7 BC9 |
CC3 CC8 |
| Ability to design and develop a learning model in a real programming environment. | AC10 AC15 |
BC5 BC6 BC7 BC8 BC9 |
CC3 CC7 CC9 |
| Master the different learning models and be able to apply them to real-world problems. | AC11 AC15 |
BC2 BC3 BC7 |
CC3 CC8 |
| Know and understand the difference between classification and regression problems. | AC10 AC11 |
BC3 BC6 BC8 |
|
| Understand how to compare the results of the different types of machine learning. | AC10 AC12 AC15 |
BC7 BC9 |
CC4 CC8 CC9 |
| Contents |
| Topic | Sub-topic |
| Supervised learning | Introduction to learning Artificial Neural Networks Support Vector Machines Decision trees Regression Instance-based learning |
| Unsupervised learning | Unsupervised learning: clustering Unsupervised neural networks |
| Reinforcement learning | Markov decision processes Reinforcement learning |
| Ensemble modeling | Basic and advanced ensemble modelling |
| Preprocessing and feature extraction techniques, regularization, model creation and evaluation. |
Preprocessing and feature extraction techniques. Regularization. Model creation and evaluation. |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Guest lecture / keynote speech | A11 A12 C4 C8 C9 | 21 | 42 | 63 |
| Laboratory practice | A13 A16 B2 B3 B5 B6 B7 C3 C7 | 12 | 24 | 36 |
| Supervised projects | B2 B3 B4 B5 B8 B9 C4 C8 C9 | 7 | 19 | 26 |
| Objective test | B3 B8 C4 C8 C9 | 2 | 20 | 22 |
| Personalized attention | 3 | 0 | 3 | |
| (*)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 |
|
|
| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| Laboratory practice | A13 A16 B2 B3 B5 B6 B7 C3 C7 | Resolution of real world problems using the methodology, for which several techniques explained in theory will be used, and the student will be stimulated to generate new ideas for the resolution of these problems. | 25 |
| Objective test | B3 B8 C4 C8 C9 | Test questions about the contents of the course, based on the different machine learning techniques and their applications. | 50 |
| Supervised projects | B2 B3 B4 B5 B8 B9 C4 C8 C9 | Writing of the report on the resolution of the real problems 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 |
| Assessment comments | |||
|
Students must achieve at least 40% of the maximum mark for each part (theory, practice) and in any case the sum of both parts must exceed 5 to pass the subject. If any of the above requirements is not met, the grade of the call will be established according to the lowest grade obtained. In the second opportunity, the evaluation will be carried out with the same criteria, and a new term will be opened for the delivery of the practical works. Grades will not be saved between academic courses. The deliveries of the practices must be made within the period established in the virtual campus and must follow the specifications indicated in the statement both for their presentation and their defense. Students will have the condition of "Presented" if you attend the theoretical test in the official evaluation period. In the case of fraudulent completion of exercises or tests, the Regulations for evaluating the academic performance of students and reviewing qualifications will be applied. In application of the corresponding regulations on plagiarism, the total or partial copy of any practice or theory exercise will suppose the suspense in the activity in which plagiarism has been detected, with a grade of 0. |
|||
| Sources of information |
| Basic |
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
Ethem Alpaydin (2004). Introduction to Machine Learning. MIT Press
David Aha (). Lazy Learning. Kluwer Academics Publishers
T.M. Mitchell (1997). Machine Learning. McGraw Hill
Richard Sutton, Andrew Barto (). Reinforcement Learning. An Introduction. MIT Press
Saso Dzeroski, Nada Lavrac (). Relational Data Mining. Springer
Andrew Webb (2002). Statistical Pattern Recognition. Wiley |
|
|
|
| Complementary | |
|
|
| Recommendations | |||
| Subjects that it is recommended to have taken before |
| Subjects that are recommended to be taken simultaneously |
| Subjects that continue the syllabus | |||
|
| Other comments | |