ECTS credits ECTS credits: 4.5
ECTS Hours Rules/Memories Student's work ECTS: 71.5 Hours of tutorials: 1 Expository Class: 15 Interactive Classroom: 25 Total: 112.5
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: First Semester
Teaching: With teaching
Enrolment: Enrollable
The aim of the subject is to acquire the skills needed to build systems that are capable of solving problems in a similar way to humans, such as diagnosing a medical problem or designing a customized referral system. The subject will focus on learning how to define the knowledge that a system needs to endow it with intelligent behavior, how to model and symbolically represent that knowledge, and how to reason automatically about those representations to achieve intelligent actions. Practical skills will be acquired with the design of intelligent systems in different areas.
- LECTURES:
- Lecture 1: First order and descriptive logic.
- Lecture 2: Rules-based systems.
- Lecture 3: Logical programming
- Lecture 4: Ontologies
- Lecture 5: Semantic networks
- Lecture 6: Knowledge graphs.
-
- ASSIGNMENTS:
- Assignment I: Representation and reasoning with first-order logic.
- Assignment II: Representation and reasoning with rules-based systems with forward chaining.
- Assignment III: Representation and reasoning with logical programming.
- Assignment IV: Representation and reasoning with ontologies and descriptive logic.
- Assignment V: Representation and reasoning with semantic networks and knowledge graphs.
BASIC REFERENCES (in order of priority)
1. José Tomás Palma Méndez & Roque Marín Morales. Artificial intelligence: methods, techniques and applications. Madrid [etc.]: McGraw Hill, cop. 2008 [Sig .: A360 15, School of Engineering]
2. Stuart Russell & Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice-Hall, 3rd edition, 2009. ISBN 0136042597 [Sig .: C10 228, School of Engineering]
COMPLEMENTARY REFERENCES
3. David Lynton Poole, Randy G. Goebel & Alan K. Mackworth. Computational intelligence: a methodological approach. London: Springer, cop. 2013 [Sig .: L2 2027 2, Biblioteca de Filosofía]
ONLINE REFERENCES
4. Artificial Intelligence (MIT), https://ocw.mit.edu/courses/electrical-engineering-and-computer-science…
5. Medical Artificial Intelligence (MIT), https://ocw.mit.edu/courses/health-sciences-and-technology/hst-947-medi…
The student will acquire a set of specific skills of knowledge representation and reasoning, but also a series of generic skills to any development of a software program and, finally, transversal skills that affect the personal skills of the student and the way he relates to other students. With this in mind, the competencies are as follows:
SPECIFIC SKILLS
* Knowledge and application of the basic algorithmic procedures of computer technologies to design solutions to problems, analyzing the suitability and complexity of the proposed algorithms.
* Knowledge, design, and efficient use of the most appropriate data types and structures to solve a problem.
* Knowledge and application of the fundamental principles and basic techniques of intelligent systems and their practical application.
* Ability to understand the environment of an organization and its needs in the field of information and communications technologies.
GENERIC SKILLS
* Ability to collect and interpret relevant data (usually within their area of study) to make judgments that include reflection on relevant social, scientific, or ethical issues.
* Ability to define, evaluate and select hardware and software platforms for the development and execution of computer systems, services and applications.
* Knowledge of basic topics and technologies that allow them to learn and develop new methods and technologies, as well as those that give them great versatility to adapt to new situations.
* Ability to solve problems with initiative, decision making, autonomy and creativity. Ability to know how to communicate and transmit the knowledge, skills and abilities of the profession.
TRANSVERSAL SKILLS
* Instrumental: Ability to analyze and synthesize. Organizational and planning skills. Oral and written communication in Galician, Spanish and English. Information management ability. Problem solving. Decision making.
* Staff: teamwork. I work in a multidisciplinary and multilingual team. Skills in interpersonal relationships. Critical thinking. Ethical commitment.
* Systematic: autonomous learning. Adaptation to new situations. Creativity. Initiative and entrepreneurial spirit. Motivation for quality. Sensitivity to environmental issues.
The teaching methodology is aimed at focusing the subject on the practical aspects of the declarative representation of knowledge and reasoning, and on the concepts that differentiate the declarative paradigm from other approaches. It seeks to understand the advantages of the declarative approach and the skills to develop a program with solvency following the methodology of Artificial Intelligence. With this in mind, two types of learning activities are distinguished: master classes and practical sessions in small groups. Thus:
* Expository teaching that will consist basically of lessons of the professor, devoted to the exhibition of the theoretical contents and to the resolution of problems or exercises. Active student participation will be sought.
* Interactive teaching will allow the acquisition of practical skills and will serve for the immediate illustration of the theoretical-practical contents, through modeling, verification or interactive programming.
Assesment system will take place in two different but complementary ways, which aim to evaluate the competencies in the practical realization of programs and the mastery of the representation of knowledge and reasoning. On the other hand, a distinction will be made between the assessment of the ordinary opportunity and that of recovery:
ORDINARY OPPORTUNITY
(1) Examination of the theoretical aspects of the subject. It will not be compulsory.
(2) Continuous assessment during the interactive classes, in which modelling, representation, reasoning and verification decisions will be worked on. In order to pass the continuous assessment of the subject, it will be necessary to pass all the proposed activities as they are proposed throughout the course. No late submissions will be allowed during the semester. The final mark can reach a maximum of 7 points out of 10 points in the final mark of the course. An optional practical will be proposed as an optional part of the continuous assessment, which may lead to an additional increase of 1 point in the mark for the practical part. On the other hand, the delivery of any interactive activity will be considered as presented in the subject. Finally, the partial or total copying of any of the deliveries will mean the suspension of the whole course.
Calculation of the final mark:
If the student has passed the continuous assessment, takes the final exam and obtains a mark higher than 5 (out of 10):
Final mark = NoteEvc + (10- NoteEvc) * NoteEx * 0.1
If the student has passed the continuous assessment and does not take the final exam or, if he/she does take the final exam, obtains a mark lower than 5 (out of 10):
Final mark = NoteEvc
If the student has not passed the continuous assessment and takes the final exam:
Final grade = NoteEx
Where NoteEvc is the final mark obtained in the continuous assessment and NoteEx is the mark obtained in the final exam of the subject.
RECOVERY OPPORTUNITY
The evaluation criteria of the theory and practice parts in the recovery opportunity will be exactly the same as for the ordinary opportunity. In order to pass the continuous assessment of the subject, it will be necessary to pass all the proposed activities as they are proposed throughout the course.
(1) Autonomous study of the concepts of the subject (15 hours). The time devoted to this study not only includes what is needed to prepare for the theoretical exam, but also the time the student needs to understand the theoretical concepts so that he can apply them correctly to the representation of knowledge and reasoning.
(2) Writing exercises and assignments (5 hours). The time devoted to this writing is related to the work that students must submit at the conclusion of each of the interactive activities, in which they must explain how they performed them.
(3) Complete the exercises of the interactive activities (15 hours). This time is necessary for the student to complete the exercises that he will not have time to finish in the practice sessions. During this time the student will be able to internalize the way to solve the problem posed in the exercise, as in the practical sessions more emphasis is placed on understanding the problem and the general way in which it will be solved, while the details needed to complete the exercises must be done in additional time of practical work.
In order to be able to take advantage of the subject and acquire its concepts with a certain fluency, it is highly advisable to take advantage of the master classes and practice sessions, as, as presented in the syllabus and teaching methodology, these activities are directly related. On the other hand, it is also highly recommended that the student explore the support material (web pages on technology, online tutorials on development environments, description of success stories, etc.) which includes additional explanations to those of the face-to-face classes and that they help to understand and strengthen the concepts of the subject.
María Jesús Taboada Iglesias
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881813561
- maria.taboada [at] usc.es
- Category
- Professor: University Professor
| Wednesday | |||
|---|---|---|---|
| 09:30-11:30 | Grupo /CLIL_01 | Spanish | IA.S2 |
| 11:30-13:30 | Grupo /CLIL_02 | Spanish | IA.S2 |
| 17:30-19:00 | Grupo /CLE_01 | Spanish | IA.S1 |
| 01.15.2025 16:00-20:00 | Grupo /CLIL_01 | Classroom A1 |
| 01.15.2025 16:00-20:00 | Grupo /CLE_01 | Classroom A1 |
| 01.15.2025 16:00-20:00 | Grupo /CLIL_02 | Classroom A1 |
| 07.03.2025 10:00-14:00 | Grupo /CLIL_02 | Classroom A4 |
| 07.03.2025 10:00-14:00 | Grupo /CLIL_01 | Classroom A4 |
| 07.03.2025 10:00-14:00 | Grupo /CLE_01 | Classroom A4 |