| Competencies / Study results |
| Code | Study programme competences / results |
| A2 | CE2 – To define, evaluate and select the architecture and the most suitable software for solving a problem in the field of bioinformatics |
| A3 | CE3 – To analyze, design, develop, implement, verify and document efficient software solutions based on an adequate knowledge of the theories, models and techniques in the field of Bioinformatics |
| A4 | CE4 - Ability to acquire, obtain, formalize and represent human knowledge in a computable form for the resolution of problems through a computer system in any field of application, particularly those related to aspects of computing, perception and action in bioinformatics applications |
| A6 | CE6 - Ability to identify software tools and most relevant bioinformatics data sources, and acquire skill in their use |
| B1 | CB6 - Own and understand knowledge that can provide a base or opportunity to be original in the development and/or application of ideas, often in a context of research |
| B2 | CB7 - Students should know how to apply the acquired knowledge and ability to problem solving in new environments or little known within broad (or multidisciplinary) contexts related to their field of study |
| B6 | CG1 -Search for and select the useful information needed to solve complex problems, driving fluently bibliographical sources for the field |
| B7 | CG2 - Maintain and extend well-founded theoretical approaches to enable the introduction and exploitation of new and advanced technologies |
| C1 | CT1 - Express oneself correctly, both orally writing, in the official languages of the autonomous community |
| C6 | CT6 - To assess critically the knowledge, technology and information available to solve the problems they face to. |
| Learning aims | |||
| Learning outcomes | Study programme competences / results | ||
| Knowledge of the different approaches used by Artificial Intelligence. | AJ4 |
BJ1 BJ7 |
CJ6 |
| To understand the basic concepts of each of them and their models. | AJ4 |
BJ1 BJ7 |
CJ6 |
| To apply the acquired knowledge to the efficient implementation of intelligent approaches in the field of study of Bioinformatics and Biomedical Engineering. | AJ2 AJ3 AJ4 AJ6 |
BJ1 BJ2 BJ6 BJ7 |
CJ1 CJ6 |
| Contents |
| Topic | Sub-topic |
| 1. Introduction | 1.1. An historical perspective 1.2. Preliminary aspects 1.3. General considerations |
| 2. Problem-solving | 2.1. Introduction to solving problems in AI 2.2. The state space concept. Searching 2.3. General characteristics of searching processes 2.4. Pure search strategies: breadth-first and depth-first 2.5. Search strategies in state space |
| 3. Structured knowledge representation | 3.1. Introduction 3.2. Declarative methods 3.3. Procedural methods 3.4. Examples and a practical case 3.5. Production systems |
| 4. Reasoning in AI | 4.1 Basics of categorical reasoning 4.2 Basics of bayesian reasoning 4.3 Fuzzy sets |
| Planning | ||||
| Methodologies / tests | Competencies / Results | Teaching hours (in-person & virtual) | Student’s personal work hours | Total hours |
| Supervised projects | A2 A3 A4 A6 B2 B6 B7 C6 C1 | 12 | 36 | 48 |
| Problem solving | A3 A4 B2 B6 B7 C1 C6 | 12 | 36 | 48 |
| Guest lecture / keynote speech | A2 A3 A4 B1 B7 C6 | 14 | 28 | 42 |
| Personalized attention | 12 | 0 | 12 | |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||
| Methodologies |
| Methodologies | Description |
| Supervised projects | Study and development of Artificial Intelligence applications in various aspects of the theoretical content of the course. |
| Problem solving | Use of Symbolic Artificial Intelligence techniques to solve problems. |
| Guest lecture / keynote speech | Teaching the contents of the course, promoting involvement of students. |
| Personalized attention |
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| Assessment | |||
| Methodologies | Competencies / Results | Description | Qualification |
| Problem solving | A3 A4 B2 B6 B7 C1 C6 | Delivery on time will be valued, as well as the attendance to the hours assigned for the internship. | 30 |
| Guest lecture / keynote speech | A2 A3 A4 B1 B7 C6 | Written test to evaluate the knowledge about the course. |
30 |
| Supervised projects | A2 A3 A4 A6 B2 B6 B7 C6 C1 | Delivery of work related to the different parts of the subject. | 40 |
| Assessment comments | |||
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It will be necessary to achieve a grade equal to or higher than 5 in the overall grade of the course to pass it, after adding the grade of the tutored work, the problem solving and the written exam. Evaluation in the second opportunity The grade of the written exam ("Guest lecture") is NOT maintained between opportunities. The overall grade of "Supervised projects" as well as the "Problem Solving" block will be maintained for the second opportunity. Only the practicals graded with SUSPENSED or NOT SUBMITTED in the first opportunity can be submitted in the second opportunity, always according to the statement proposed for the link. Enrollment with academic waiver Students enrolled with academic dispensation are exempted from attending classes, but must submit the evaluable activities under the specific conditions and deadlines that will be established during the course. It is the student's responsibility to inform the faculty of this circumstance. |
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| Sources of information |
| Basic |
Moret et al. (20015). Fundamentos de inteligencia artificial. Servicio de publicaciones de la UDC (2ª ed, 2ª imp)
José T. Palma, Roque Marín Morales et al. (2008). Inteligencia artificial - Técnicas, métodos y aplicaciones. McGraw Hill (1ª ed.)
Russell & Norvig (2004). Inteligencia artificial: un enfoque moderno. . Pearson (2ª ed) |
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| Subjects that it is recommended to have taken before |
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