This course requires the ability to work with abstract concepts and a certain ability to solve problems autonomously.
A level of content in previous courses of the Degree is required:
Group work skills and basic knowledge (reading and comprehension) of English are also required.
This course is integrated in the subject "Software Engineering, Information Systems and Intelligent Systems" of the study plan and represents the gateway or presentation to the techniques of Artificial Intelligence within the Degree. These techniques are now included among those most required for solving complex problems: decision making; diagnostic, monitoring and control systems; web search engines; semantic web or web 2.0; recommendation systems; automatic learning; mining and data analysis; vision and robotics; etc.
There is no doubt that the subject requires other previous subjects (discrete mathematics, logic, all of the programming subject), is a requirement for subjects located later in the Degree (data mining, knowledge-based systems, multi-agent systems, artificial vision and robotics), and is a co-requirement to globally define a software project with other courses such as information systems, databases and software engineering.
Course competences | |
---|---|
Code | Description |
BA04 | Basic knowledge about the uses and programming of computers, operating systems, data bases, and digital programmes with applications in engineering. |
CO15 | Knowledge and application of fundamental principles and basic techniques on intelligent systems and their practical applications. |
INS01 | Analysis, synthesis, and assessment skills. |
INS03 | Ability to manage information and data. |
INS04 | Problem solving skills by the application of engineering techniques. |
INS05 | Argumentative skills to logically justify and explain decisions and opinions. |
PER01 | Team work abilities. |
SIS01 | Critical thinking. |
SIS03 | Autonomous learning. |
SIS04 | Adaptation to new scenarios. |
SIS05 | Creativity. |
SIS09 | Care for quality. |
UCLM02 | Ability to use Information and Communication Technologies. |
Course learning outcomes | |
---|---|
Description | |
Knowledge about the basic principles and techniques of intelligent systems and their practical application. | |
Additional outcomes | |
Not established. |
A laboratory practice:
Resolution of a problem by means of different strategies of search in a space of states.
Training Activity | Methodology | Related Competences | ECTS | Hours | As | Com | Description | |
Class Attendance (theory) [ON-SITE] | Lectures | BA04 CO15 SIS01 SIS09 UCLM02 | 0.72 | 18 | N | N | Teaching of the subject matter by lecturer (MAG) | |
Individual tutoring sessions [ON-SITE] | BA04 CO15 UCLM02 | 0.18 | 4.5 | N | N | Individual or small group tutoring in lecturer's office, classroom or laboratory (TUT) | ||
Study and Exam Preparation [OFF-SITE] | Self-study | BA04 CO15 SIS01 SIS09 UCLM02 | 2.1 | 52.5 | N | N | Self-study (EST) | |
Other off-site activity [OFF-SITE] | Practical or hands-on activities | BA04 CO15 INS03 INS04 INS05 PER01 SIS03 SIS04 SIS05 UCLM02 | 0.6 | 15 | N | N | Lab practical preparation (PLAB) | |
Problem solving and/or case studies [ON-SITE] | Problem solving and exercises | BA04 CO15 INS01 INS04 PER01 SIS03 SIS09 | 0.6 | 15 | Y | N | Worked example problems and cases resolution by the lecturer and the students (PRO) | |
Writing of reports or projects [OFF-SITE] | Self-study | BA04 CO15 INS01 INS04 INS05 PER01 SIS03 | 0.9 | 22.5 | Y | N | Preparation of essays on topics proposed by lecturer (RES) | |
Laboratory practice or sessions [ON-SITE] | Practical or hands-on activities | BA04 CO15 INS03 INS04 INS05 PER01 SIS03 SIS05 SIS09 UCLM02 | 0.6 | 15 | Y | Y | Realization of practicals in laboratory /computing room (LAB) | |
Other on-site activities [ON-SITE] | Assessment tests | BA04 CO15 INS01 INS04 INS05 UCLM02 | 0.15 | 3.75 | Y | Y | Partial test 1 of the first half of the syllabus of the subject (EVA) | |
Other on-site activities [ON-SITE] | Assessment tests | BA04 CO15 INS01 INS04 INS05 UCLM02 | 0.15 | 3.75 | Y | Y | Partial test 2 of the second half of the syllabus of the subject (EVA) | |
Total: | 6 | 150 | ||||||
Total credits of in-class work: 2.4 | Total class time hours: 60 | |||||||
Total credits of out of class work: 3.6 | Total hours of out of class work: 90 |
As: Assessable training activity Com: Training activity of compulsory overcoming (It will be essential to overcome both continuous and non-continuous assessment).
Evaluation System | Continuous assessment | Non-continuous evaluation * | Description |
Test | 20.00% | 0.00% | Partial Test 1. Compulsory activity that can be retaken (rescheduling). To be carried out at the end of the first half of the teaching period |
Theoretical papers assessment | 15.00% | 15.00% | Non-compulsory activity that can be retaken. To be carried out before end of teaching period |
Test | 30.00% | 0.00% | Partial Test 2. Compulsory activity that can be retaken. To be carried out within the planned dates of the final exam call. The Partial Test 1 retake will be performed at this date |
Assessment of active participation | 10.00% | 0.00% | Non-compulsory activity that can be retaken (rescheduling). To be carried out in the theory/laboratory sessions for the students of the continuous modality. |
Final test | 0.00% | 50.00% | Compulsory activity that can be retaken (rescheduling) to be carried out within the planned exam dates of the final exam call (convocatoria ordinaria). |
Laboratory sessions | 25.00% | 25.00% | Compulsory activity that can be retaken. To be carried out during lab sessions |
Total: | 100.00% | 90.00% |
Not related to the syllabus/contents | |
---|---|
Hours | hours |
General comments about the planning: | The subject is taught in 3 x 1,5 hour sessions per week. |