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B2_That students know how to apply their knowledge to their job or vocation in a professional way and have the skills they demonstrate by developing and defending arguments and solving problems within their area of study
B3_Students have the ability to gather and interpret relevant data (usually within their area of study), to make judgments that include reflection on relevant social, scientific or ethical issues
B5_That students have developed those learning skills necessary to undertake further studies with a high degree of autonomy
EFB3_Ability to understand and master the basic concepts of discrete mathematics, logic, algorithms and computational complexity, and their application for solving engineering problems
T1_That students know a third language, which will be preferably English, with an adequate level of oral and written form, according to the needs of the graduates in each degree
T2_That students have the ability to work as members of an interdisciplinary team either as one more member, or performing management tasks in order to contribute to developing projects with pragmatism and a sense of responsibility, making commitments taking into account the available resources
Artificial intelligence is a discipline that studies intelligent agents, understanding as such those devices (software and / or hardware) that perceive the environment, reason and take action to achieve their goals. In recent years artificial intelligence has hit the industry with great force and many analysts believe it will be the main factor in the upcoming industrial revolution.
During the course there is an introduction to the most classic Artificial Intelligence with an in-depth study of the search and logic algorithms that are used today to solve countless problems. For example: google search, google maps, Amazon and Netflix recommending systems, making schedules, autonomous vehicles, video games, and a long etc. The last chapter gives a brief introduction to machine learning, more specifically to the classification and clustering that are the basis of data analysis algorithms.
This subject has methodological and digital resources to make possible its continuity in non-contact mode in the case of being necessary for reasons related to the Covid-19. In this way, the achievement of the same knowledge and skills that are specified in this teaching plan will be ensured.
At a general level, this subject contributes to the following learning outcomes specified for the subject to which it belongs (Algorithmic and Programming)
At a more specific level, at the end of the course the student must be able to:
All the theoretical concepts of the subject will be exposed in theory classes (large groups). In these classes, and at the discretion of the teachers, exercises and problems of a more practical nature will also be solved. Likewise, and always at the discretion of the teachers, students may be asked to solve, individually or in groups, short problems and / or exercises. These activities, brief and optional, will serve the student as a tool for self-assessment of their achievement of the contents of the subject and can be used by the teacher to make decisions about the final grade of the student good and never to the detriment of the numerical grade calculated according to the grading system specified by the subject.
Concepts of a more practical nature and everything that can essentially be considered the practical application of theoretical concepts will be worked on more intensively in small (laboratory) groups. In the sessions scheduled for this purpose, the appropriate tools will be given to solve the scheduled activities well and it is expected that these will be extended from a temporal point of view, beyond the laboratory hours and that, consequently, ¨ence, students must complete them during the time of autonomous learning.
It will be made available to students activities of a completely optional nature that will help them prepare and prepare for those of a compulsory nature.
1 Introduction to Artificial Intelligence
1.1 History
1.2 Applications
1.3 Ethics and feminism
2 Troubleshooting
2.1 Troubleshooting
2.2 Uninformed search: BFS, DFS
2.3 Informed search: voracious search, algorithm A
2.4 Heuristic functions
2.5 Search in games: minimax, alpha-beta prunning
2.6 Satisfaction of restrictions
3 Logic
3.1 Representation of knowledge: facts and rules
3.2 Inference or reasoning algorithms
4 Machine learning
4.1 Supervised. Classification: N-nearest neighbors, decision trees, Naive Bayes
4.2 Unsupervised. Clustering: K-means
In order to gather evidence of the achievement of the expected learning outcomes, the following evaluative activities will be carried out:
Practices: there will be a maximum of four internships (related to all skills)
All the common and specific competences will be worked on in the practices: CIN1, CIN3, CIN7, CIN8, CIN15 and EFB3, as well as the basic B2, B3, B4 and the transversal T1 and T2
Written test: individual exam on theory and problem solving seen in class. This test collects evidence of all learning outcomes.
All the common and specific competences will be worked on in the written test: CIN1, CIN3, CIN7, CIN8, CIN15 and EFB3, as well as the basic B2, B3 and B5
The following are the most important aspects of each competence assigned to the subject:
In order to pass (pass) the assessment activities, students will have to demonstrate
Assessment:
Final grade calculation (NF):
Recovery:
The written test (PR_E) can be retrieved. The final grade will be calculated as set out above with the written test recovery grade.
Normative:
Russel, Stuard and Norvic, Peter (2013), "Artificial Intelligence: a moderate approach". (3rd edition) Prentice Hall.