This subject is based on the skills and knowledge acquired in the subjects of Intelligent Systems, Knowledge-Based Systems and Algorithm Design (Computer Science Specialization)
This subject is elective for the degree. It is related to the subjects in the field of Artificial Intelligence and can serve as a complement to subjects in the intensification of Computer Science as Data Mining. It also presents an introduction to advanced data analysis ( Big Data Analytics).
Course competences | |
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Code | Description |
CM07 | Ability to know and develop computational learning techniques, and design and implement applications and systems which could use them, including the ones for the automatic extraction of information and knowledge from great batches of information. |
INS01 | Analysis, synthesis, and assessment skills. |
INS02 | Organising and planning 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. |
PER02 | Ability to work in multidisciplinary teams. |
PER04 | Interpersonal relationship skills. |
SIS01 | Critical thinking. |
SIS03 | Autonomous learning. |
SIS04 | Adaptation to new scenarios. |
Course learning outcomes | |
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Description | |
Knowledge of the fundamental aspects and techniques of automatic learning within the supervised, unsupervised and reinforcement paradigms. Ability to explain the distinctions between different learning styles and to determine which is most appropriate for a given problem domain. | |
Additional outcomes | |
Description | |
Comprehensive view of the types of machine learning algorithms and understand their evolution Knowledge of how to make use of machine learning algorithms, knowledge representation and data mining, applying them creatively in the solution of problems where inferred knowledge is required and parallel distributed processing techniques using up-to-date artificial intelligence technologies. Ability to apply basic concepts of human learning in the solution of machine learning problems. |
Completion of a capstone project encompassing the following data analysis tasks: transformation, exploratory data analysis, unsupervised and supervised learning techniques.
Training Activity | Methodology | Related Competences | ECTS | Hours | As | Com | Description | |
Class Attendance (theory) [ON-SITE] | Lectures | CM07 INS01 INS02 INS03 INS04 INS05 SIS01 SIS03 SIS04 | 0.72 | 18 | N | N | Teaching of the subject matter by lecturer (MAG) | |
Individual tutoring sessions [ON-SITE] | CM07 INS01 INS02 INS03 INS04 INS05 SIS01 SIS03 SIS04 | 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 | CM07 INS01 INS02 INS03 INS04 INS05 SIS01 SIS03 SIS04 | 2.1 | 52.5 | N | N | Self-study (EST) | |
Other off-site activity [OFF-SITE] | Practical or hands-on activities | CM07 INS01 INS02 INS03 INS04 INS05 PER01 PER02 PER04 SIS01 SIS03 SIS04 | 0.6 | 15 | N | N | Lab practical preparation (PLAB) | |
Problem solving and/or case studies [ON-SITE] | Problem solving and exercises | CM07 INS01 INS02 INS03 INS04 INS05 SIS01 SIS03 SIS04 | 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 | CM07 INS01 INS02 INS03 INS04 INS05 PER01 PER02 PER04 SIS01 SIS03 SIS04 | 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 | CM07 INS01 INS02 INS03 INS04 INS05 SIS01 SIS03 SIS04 | 0.6 | 15 | Y | Y | Realization of practicals in laboratory /computing room (LAB) | |
Progress test [ON-SITE] | Assessment tests | CM07 INS01 INS02 INS03 INS04 INS05 SIS01 SIS03 SIS04 | 0.1 | 2.5 | Y | N | Progress test 1 of the first third of the syllabus of the subject (EVA) | |
Progress test [ON-SITE] | Assessment tests | CM07 INS01 INS02 INS03 INS04 INS05 SIS01 SIS03 SIS04 | 0.1 | 2.5 | Y | N | Progress test 2 of the two first thirds of the syllabus of the subject (EVA) | |
Progress test [ON-SITE] | Assessment tests | CM07 INS01 INS02 INS03 INS04 INS05 SIS01 SIS03 SIS04 | 0.1 | 2.5 | Y | N | Progress test 3 of the complete 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 |
Progress Tests | 7.50% | 0.00% | Progress test 1. Non-compulsory activity that can be retaken (rescheduling). To be carried out at the end of the first third of the teaching period. |
Progress Tests | 15.00% | 0.00% | Progress test 2 Non-compulsory activity that can be retaken. To be carried out at the end of the second third of the teaching period. |
Progress Tests | 27.50% | 0.00% | Progress test 3. Non-compulsory activity that can be retaken. To be carried out during the non-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 |
Laboratory sessions | 25.00% | 25.00% | Compulsory activity that can be retaken. To be carried out during lab sessions |
Oral presentations assessment | 10.00% | 10.00% | Non-compulsory activity that can be retaken. The students in the continuous mode will be evaluated in theory/laboratory sessions The students of non-continuous mode will be evaluated from this activity through of an alternative system. |
Final test | 0.00% | 50.00% | Compulsory activity that can be retaken to be carried out on the date scheduled for the final ordinary exam. |
Total: | 100.00% | 100.00% |
Not related to the syllabus/contents | |
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Hours | hours |
General comments about the planning: | The course is taught in three weekly sessions of 1.5 hours. |