Guías Docentes Electrónicas
1. General information
Course:
INTELLIGENT SYSTEMS
Code:
42321
Type:
CORE COURSE
ECTS credits:
6
Degree:
347 - DEGREE PROGRAMME IN COMPUTER SCIENCE ENGINEERING (CR)
Academic year:
2021-22
Center:
108 - SCHOOL OF COMPUTER SCIENCE OF C. REAL
Group(s):
20  21  22 
Year:
3
Duration:
First quarter
Main language:
English
Second language:
Spanish
Use of additional languages:
English Friendly:
N
Web site:
http://campusvirtual.uclm.es
Bilingual:
Y
Lecturer: LUIS JIMENEZ LINARES - Group(s): 21 
Building/Office
Department
Phone number
Email
Office hours
Fermín Caballero / 3.16
TECNOLOGÍAS Y SISTEMAS DE INFORMACIÓN
3704
luis.jimenez@uclm.es
https://esi.uclm.es/categories/profesorado-y-tutorias

Lecturer: JESÚS RAMÓN OVIEDO LAMA - Group(s): 22 
Building/Office
Department
Phone number
Email
Office hours
TECNOLOGÍAS Y SISTEMAS DE INFORMACIÓN
Jesus.Oviedo@uclm.es

Lecturer: LUIS RODRIGUEZ BENITEZ - Group(s): 20  22 
Building/Office
Department
Phone number
Email
Office hours
Fermín Caballero / 2.05
TECNOLOGÍAS Y SISTEMAS DE INFORMACIÓN
3731
luis.rodriguez@uclm.es
https://esi.uclm.es/categories/profesorado-y-tutorias

2. Pre-Requisites

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:

  •     Basic knowledge in discrete mathematics and probability.
  •     Ability to pose and solve problems logically (first-order logic, inference, resolution, etc.).
  •     Mastery of typical data structures (graphs, trees, etc.) as well as the algorithms necessary for their management.
  •     Knowledge of basic algorithmic techniques, software engineering principles, algorithm cost analysis and algorithmic complexity.
  •     Programming fluency with high level object-oriented languages (e.g. Java).

Group work skills and basic knowledge (reading and comprehension) of English are also required.

3. Justification in the curriculum, relation to other subjects and to the profession

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.


4. Degree competences achieved in this course
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.
5. Objectives or Learning Outcomes
Course learning outcomes
Description
Knowledge about the basic principles and techniques of intelligent systems and their practical application.
Additional outcomes
Not established.
6. Units / Contents
  • Unit 1: Introduction to intelligent agents and intelligent systems
  • Unit 2: Problem solving through search.
  • Unit 3: Informed search and exploration.
  • Unit 4: Constraint satisfaction problems.
  • Unit 5: Search among adversaries.
  • Unit 6: Reinforcement Learning
ADDITIONAL COMMENTS, REMARKS

A laboratory practice:

Resolution of a problem by means of different strategies of search in a space of states.


7. Activities, Units/Modules and Methodology
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: 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).

8. Evaluation criteria and Grading System
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).
Total: 100.00% 100.00%  
According to art. 6 of the UCLM Student Evaluation Regulations, it must be provided to students who cannot regularly attend face-to-face training activities the passing of the subject, having the right (art. 13.2) to be globally graded, in 2 annual calls per subject , an ordinary and an extraordinary one (evaluating 100% of the competences).

Evaluation criteria for the final exam:
  • Continuous assessment:
    In compulsory activities, a minimum mark of 40% is required in order to pass that activity and have the possibility to therefore pass the entire subject. The evaluation of the activities will be global and therefore must be quantified by means of a single mark. In the case of the activities that may be retaken (i.e., rescheduling), an alternative activity or test will be offered in the resit/retake exam call (convocatoria extraordinaria).

    The final exam will be common for all the theory/laboratory groups of the subject and will be evaluated by the lecturers of the subject in a serial way, i.e., each part of the final exam will be evaluated by the same lecturer for all the students. A student is considered to pass the subject if she/he obtains a minimum of 50 points out of 100, taking into account the points obtained in all the evaluable activities, and also has passed all the compulsory activities.

    For students who do not pass the subject in the final exam call (convocatoria ordinaria), the marks of activities already passed will be conserved for the resit/retake examcall (convocatoria extraordinaria). If an activity is not recoverable, its assessment will be preserved for the resit/retake exam call (convocatoria extraordinaria) even if it has not been passed. In the case of the passed recoverable activities, the student will have the opportunity to receive an alternative evaluation of those activities in the resit/retake exam call and, in that case, the final grade of the activity will correspond to the latter grade obtained. The mark of the passed activities in any call, except for the final exam, will be conserved for the subsequent academic year at the request of the student, provided that mark is equal or greater than 50% and that the activities and evaluation criteria of the subject remain unchanged prior to the beginning of that academic year. The failure of a student to attend the final exam will automatically result in her/him receiving a "Failure to attend" (no presentado). If the student has not passed any compulsory evaluation activity, the maximum final grade will be 40%
  • Non-continuous evaluation:
    Students may apply at the beginning of the semester for the non-continuous assessment mode. In the same way, the student may change to the non-continuous evaluation mode as long as she/he has not participated during the teaching period in evaluable activities that together account for at least 50% of the total mark of the subject. If a student has reached this 50% of the total obtainable mark or the teaching period is over, she/he will be considered in continuous assessment without the possibility of changing to non-continuous evaluation mode.

    Students who take the non-continuous evaluation mode will be globally graded, in 2 annual calls per subject, an ordinary and an extraordinary one (evaluating 100% of the competences), through the assessment systems indicated in the column "Non-continuous evaluation".

    In the "non-continuous evaluation" mode, it is not compulsory to keep the mark obtained by the student in the activities or tests (progress test or partial test) taken in the continuous assessment mode.

Specifications for the resit/retake exam:
Evaluation tests will be conducted for all recoverable activities.
Specifications for the second resit / retake exam:
Same characteristics as the resit/retake exam call.
9. Assignments, course calendar and important dates
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.
10. Bibliography and Sources
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  



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