Study programme competencies |
Code
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Study programme competences
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A2 |
Capacidad para resolver problemas de inteligencia artificial que precisen algoritmos, aplicando correctamente metodologías de desarrollo software y diseño centrado en usuario/a. |
A3 |
Capacidad para comprender y dominar los conceptos básicos de lógica, gramáticas y lenguajes formales para analizar y mejorar las soluciones basadas en inteligencia artificial. |
B2 |
Que el alumnado sepa aplicar sus conocimientos a su trabajo o vocación de una forma profesional y posea las competencias que suelen demostrarse por medio de la elaboración y defensa de argumentos y la resolución de problemas dentro de su área de estudio. |
B4 |
Que el alumnado pueda transmitir información, ideas, problemas y soluciones a un público tanto especializado como no especializado. |
B5 |
Que el alumnado haya desarrollado aquellas habilidades de aprendizaje necesarias para emprender estudios posteriores con un alto grado de autonomía. |
B6 |
Capacidad para concebir, redactar, organizar, planificar, y desarrollar modelos, aplicaciones y servicios en el ámbito de la inteligencia artificial, identificando objetivos, prioridades, plazos recursos y riesgos, y controlando los procesos establecidos. |
B7 |
Capacidad para resolver problemas con iniciativa, toma de decisiones, autonomía y creatividad. |
B8 |
Capacidad para diseñar y crear modelos y soluciones de calidad basadas en Inteligencia Artificial que sean eficientes, robustas, transparentes y responsables. |
B9 |
Capacidad para seleccionar y justificar los métodos y técnicas adecuadas para resolver un problema concreto, o para desarrollar y proponer nuevos métodos basados en inteligencia artificial. |
C2 |
Capacidad de trabajo en equipo, en entornos interdisciplinares y gestionando conflictos. |
C3 |
Capacidad para crear nuevos modelos y soluciones de forma autónoma y creativa, adaptándose a nuevas situaciones. Iniciativa y espíritu emprendedor. |
C6 |
Capacidad para integrar aspectos jurídicos, sociales, ambientales y económicos inherentes a la inteligencia artificial, analizando sus impactos, y comprometiéndose con la búsqueda de soluciones compatibles con un desarrollo sostenible. |
Learning aims |
Learning outcomes |
Study programme competences |
Carry out the process that allows, from the abstraction, to implement high quality code |
A2 A3
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B2 B6 B7 B8 B9
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C2 C3 C6
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Acquire skills to solve problems in a methodological and practical way |
A2 A3
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B5 B6 B7 B8 B9
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C3 C6
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Establish clearly and unambiguously the client's needs and constraints when developing requirements for a software project |
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B4 B7
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C2 C6
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Analyze the alternatives to deal with and identify which aspects can be addressed with AI and which cannot |
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B2 B4 B7 B9
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C3 C6
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Understand the principles needed to build complete, scalable, and robust, user-centered solutions where AI components fit together as part of a whole |
A2
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B8 B9
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C6
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Be able to identify and understand models and designs of architectures and components to enable effective communication between software and data engineers |
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B4
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C2
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Handle testing techniques and tools to ensure the quality of the results |
A3
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B8 B9
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C6
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Contents |
Topic |
Sub-topic |
Introduction to the principles of Software Engineering |
- Introduction: basic concepts |
Software life cycle |
- Introduction: concepts and terminology
- Life cycle types
- Phases of life cycles
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Agile approaches |
- Introduction: agile approaches
- Scrum |
Requirements gathering, analysis techniques |
- Introduction: software requirements
- Specification of requirements |
Architecture and component modeling |
- Introduction: software models
- Modeling techniques
- Architecture models
- Component models |
Software development |
- Introduction: software and tools
- Software management
- Good practices |
Principles, processes and activities of software testing |
- Introduction: principles of software testing
- Unit testin
- Integration tests
- Acceptance tests |
Planning |
Methodologies / tests |
Competencies |
Ordinary class hours |
Student’s personal work hours |
Total hours |
Guest lecture / keynote speech |
A2 A3 B2 B6 B8 B9 C3 C6 |
30 |
30 |
60 |
Laboratory practice |
A2 A3 B2 B4 B5 B6 B7 B8 B9 C2 C3 C6 |
30 |
48 |
78 |
Objective test |
A2 A3 B2 B6 B8 B9 C6 |
2 |
10 |
12 |
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Personalized attention |
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0 |
0 |
0 |
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(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Methodologies |
Description |
Guest lecture / keynote speech |
In the lectures, the professor will present the theoretical knowledge related to the different subjects of the subject |
Laboratory practice |
The practical classes will be dedicated to carrying out individual and groupal works related to the subject presented in the lectures, through the use of computer tools |
Objective test |
In the objective test, the theoretical and practical knowledge acquired in the subject will be evaluated |
Personalized attention |
Methodologies
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Laboratory practice |
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Description |
In the laboratory practices, both independent work and group work will be carried out, where the students will put into practice the knowledge acquired in the subject. During the completion of these tasks, the teacher will advise the students both individually and in groups to resolve any doubts or problems that the students encounter in the application of the concepts learned.
In general, student participation will be encouraged, both in practical classes and in lectures, so that the concepts presented are understood by the students.
Students with partial enrollment and/or with academic dispensation will carry out the work and tasks individually, with deliveries on the dates set by the teacher, and will have personalized attention during the tutoring hours, to clarify doubts about the work and also about the theoretical framework and practical subject. |
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Assessment |
Methodologies
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Competencies |
Description
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Qualification
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Laboratory practice |
A2 A3 B2 B4 B5 B6 B7 B8 B9 C2 C3 C6 |
Both the individual and group work of the students will be assessed in the practices.
To demonstrate the work done, the students will present documentary evidence both individually and as a group, and they will make a group presentation of the work done.
Both in the documents (individual and group) and in the group presentation, the following aspects will be assessed:
- Technical level.
- Completeness, clarity and justifications of the work.
- Mastery of acquired knowledge.
- Spelling and writing.
At an individual level, active participation will also be valued. |
50 |
Objective test |
A2 A3 B2 B6 B8 B9 C6 |
In this test, mastery of the theoretical and practical knowledge of the subject will be evaluated through an individual exam. |
50 |
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Assessment comments |
GradingEach student's final grade will be obtained as follows: - Individual written exam: 50%
- Laboratory practice: 50%, which will be distributed as follows:
- Individual grading: 20%.
- Group grading: 30%
To pass the course it is necessary to obtain a minimum overall score of 5 out of 10 and to meet the following restrictions: - It is necessary to have a minimum of 4.5 out of 10 in the practice, both in the individual part and in the group independently.
- It is necessary to have a minimum of 4.5 out of 10 in the objective test.
In the event that any of the above minimums are not met and the final grade calculated as indicated exceeds 4.0, the grade that will appear for the subject will be 4.0. Second chanceStudents who do not pass the subject may take the objective test in the call for a second chance. Given the nature of the continuous evaluation of the practice, this part cannot be recovered. Academic FraudThe fraudulent completion of tests or assessment activities, once verified, will directly involve the qualification of failure in the call in which it is committed: the student will be qualified with failure (numerical grade 0) in the corresponding call of the academic year, whether the commission of the foul occurs in the first opportunity as in the second. For this, your qualification will be modified in the first opportunity report, if necessary. Student with recognition of academic dispensationAccording to what is established in the Norma que regula o réxime de dedicación ao estudo e a permanencia e a progresión dos estudantes de grao e máster universitario na universidade da Coruña (aprobada polo consello social do 04/05/2017): - You must bring it to the attention of the teacher in the first week of class, or, if this was not possible, within a period of no more than 7 days after the recognition was granted.
- They will have to carry out, individually, all the activities/work proposed throughout the course and hand them in on the dates established by the teacher. If the delivery is not completed by the specified date, it will be considered not submitted.
- The grade will be the weighted average of the marks for the activities and work carried out during the course and the mark of the test carried out on the date of the official exam calendar, taking into account the weighting collected in this guide, with the minimum score also being obtained in each of the parts collected in this practice to be able to pass the subject. In the event that one of the parts is not passed in the first opportunity, they must repeat the objective test in the second opportunity, the laboratory practices not being repeatable because they represent a comparative grievance for students without a dispensation.
Other considerations- As stated in the different regulations for university teaching, the gender perspective must be incorporated in this subject: non-sexist language will be used, bibliography by authors of both sexes will be used as much as possible, and students will be encouraged to participate in class. ..
- Work will be done to identify and modify prejudices and sexist attitudes and influence the environment to modify them and promote values of respect and equality.
- Situations of discrimination based on gender must be detected and actions and measures will be proposed to correct them.
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Sources of information |
Basic
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Eduardo Antonio Moraleda Gil ; Sebastián Rubén Gómez Palomo (2019 (2ª ed)). Aproximación a la Ingeniería del Software. Cerasa
Roger S. Pressman (2010 (7ª ed)). Ingeniería del Software: un enfoque práctico. McGraw-Hill
Hein Smith (2018). Scrum: The Ultimate Beginner's Guide To Learn And Master Scrum Agile Framework. CreateSpace Independent Publishing Platform |
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Complementary
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Robert C. Martin (2008). Clean Code: A Handbook of Agile Software Craftsmanship. Pearson
Chris Riccomini, Dmitriy Ryaboy (2021). he Missing README: A Guide for the New Software Engineer. No Starch Press
Jeff Sutherland and Scrum, Inc. (2014). Scrum: The Art of Doing Twice the Work in Half the Time. Crown Business |
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Recommendations |
Subjects that it is recommended to have taken before |
Programming I/614G03006 | Programming II/614G03007 |
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Subjects that are recommended to be taken simultaneously |
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Subjects that continue the syllabus |
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