This course is based on the competencies and knowledge obtained in the previous courses:
As a general suggestion it is strongly recommended:
This course is integrated into the “Programming” subject within the common Module of the Computer Science branch of the “Grado en Ingeniería Informática”. The
course provides the basis for solving real and complex problems. Therefore, the course is key to later years courses, especially to
Course competences | |
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Code | Description |
BA03 | Ability to understand basic concepts about discrete mathematics, logic, algorithms, computational complexity, and their applications to solve engineering problems. |
CO06 | Knowledge and application of basic algorithms in digital technologies for the development of solutions, analysing their appropriateness and complexity. |
CO07 | Knowledge, design, and efficient use of types of data and structures which arise as most appropriate in problem solving. |
INS01 | Analysis, synthesis, and assessment skills. |
INS04 | Problem solving skills by the application of engineering techniques. |
PER01 | Team work abilities. |
PER02 | Ability to work in an international context. |
PER04 | Interpersonal relationship skills. |
PER05 | Acknowledgement of human diversity, equal rights, and cultural variety. |
SIS01 | Critical thinking. |
SIS03 | Autonomous learning. |
UCLM02 | Ability to use Information and Communication Technologies. |
Course learning outcomes | |
---|---|
Description | |
Design of solutions for problems by the analysis of appropriateness and complexity of suggested algorithms. | |
Resolution of problems throughout basic techniques of algorithm design. | |
Additional outcomes | |
Description | |
Training Activity | Methodology | Related Competences (only degrees before RD 822/2021) | ECTS | Hours | As | Com | Description | |
Class Attendance (theory) [ON-SITE] | Lectures | BA03 CO06 CO07 | 0.72 | 18 | N | N | ||
Individual tutoring sessions [ON-SITE] | BA03 CO06 CO07 UCLM02 | 0.18 | 4.5 | N | N | |||
Study and Exam Preparation [OFF-SITE] | Self-study | BA03 CO06 CO07 SIS01 SIS03 | 2.1 | 52.5 | N | N | ||
Other off-site activity [OFF-SITE] | Self-study | BA03 CO06 CO07 INS01 INS04 PER01 PER02 PER04 PER05 SIS03 | 0.6 | 15 | Y | N | ||
Problem solving and/or case studies [ON-SITE] | Problem solving and exercises | BA03 CO06 CO07 INS04 PER01 PER02 PER04 PER05 SIS01 SIS03 UCLM02 | 0.6 | 15 | Y | N | ||
Writing of reports or projects [OFF-SITE] | Self-study | BA03 CO06 CO07 INS01 INS04 PER02 PER04 PER05 | 0.9 | 22.5 | Y | N | ||
Laboratory practice or sessions [ON-SITE] | Practical or hands-on activities | BA03 CO06 CO07 INS04 PER01 PER02 PER04 PER05 | 0.6 | 15 | Y | Y | ||
Final test [ON-SITE] | Assessment tests | BA03 CO06 CO07 INS01 INS04 PER01 PER02 | 0.3 | 7.5 | 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).
Evaluation System | Continuous assessment | Non-continuous evaluation * | Description |
Final test | 50.00% | 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) |
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 |
Assessment of active participation | 10.00% | 10.00% | Non-compulsory activity that can be retaken. To be carried out during the theory/lab sessions by the continuous assesment students. Non-continuous evaluation students will be evaluated with an alternative system. |
Total: | 100.00% | 100.00% |
Not related to the syllabus/contents | |
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Hours | hours |
General comments about the planning: | The subject is taught in 3 x 1,5 hour sessions per week. The planning can be modified in the event of unforeseen causes. |
Author(s) | Title | Book/Journal | Citv | Publishing house | ISBN | Year | Description | Link | Catálogo biblioteca |
---|---|---|---|---|---|---|---|---|---|
José Luis Verdegay Galdeano | Lecciones de Algorítmica | Editorial Técnica Avicam | 978-8416535897 | 2017 | |||||
Michael T. Goodrich , Roberto Tamassia , Michael H. Goldwasser | Data Structures and Algorithms in Java - 6th Edition International Student Version | John Wiley & Sons Inc | 978-1118808573 | 2014 | |||||
R. Sedgewick, K. Wayne | Algorithms, 4th Edition | New Jersey, USA | Addison-Wesley | 978-0321573513 | 2011 | http://algs4.cs.princeton.edu/home/ | |||
T Cormen, C Leiserson, R Rivest and C Stein | Introduction to Algorithms | Cambridge, MA, USA | MIT Press | 978-0262533058 | 2009 | https://mitpress.mit.edu/books/introduction-algorithms |