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 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.
|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.|
|SIS04||Adaptation to new scenarios.|
|SIS09||Care for quality.|
|UCLM02||Ability to use Information and Communication Technologies.|
|Course learning outcomes|
|Knowledge about the basic principles and techniques of intelligent systems and their practical application.|
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||Taking two partial tests (continuous assessment) or a final test (non-continuous assessment) EVA|
|Other on-site activities [ON-SITE]||Assessment tests||0.15||3.75||Y||Y|
|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|
|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|
|Laboratory sessions||25.00%||25.00%||Compulsory activity that can be retaken. To be carried out during lab sessions or by self-study.|
|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|
|Assessment of active participation||10.00%||10.00%||Non-compulsory activity that can be retaken (rescheduling). To be carried out in the theory/laboratory sessions for the students of the continuous modality. The students of non continuous modality will be evaluated of this activity through an alternative system in the ordinary call|
|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).|
|Not related to the syllabus/contents|
|General comments about the planning:||The subject is taught in 3 x 1,5 hour sessions per week.|
|Author(s)||Title||Book/Journal||Citv||Publishing house||ISBN||Year||Description||Link||Catálogo biblioteca|
|Nilsson, Nils J.||Inteligencia artificial: una nueva síntesis||McGraw-Hill||9788448128241||2001|
|Patrick Henry Winston||Inteligencia Artificial||Addison-wesley||0-201-51876-7||1994|
|S.J. Russell y P. Norvig||Inteligencia artificial : un enfoque moderno||McGraw-Hill||842054003x||2004|