TecnoCampus Foundation
eCampus
University Business
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General information

Subject type: Mandatory

Coordinator: Vladimir Bellavista Parent

Trimester: Third term

Credits: 4

Teaching staff: 

Sandra Obiol Madrid

Academic year: 2025

Teaching course: 3

Languages ​​of instruction

  • Catalan

The subject will be taught in Catalan. Students will be able to address the teacher in the language that is most comfortable for them.

Some content, transparencies and bibliography will be in English.

Competencies / Learning Outcomes

Basic skills

  • 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

Specific skills

  • EFB3_Ability to understand and master the basic concepts of discrete mathematics, logic, algorithms and computational complexity, and their application for solving engineering problems

Transversal competences

  • 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

Presentation of the subject

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.

The classroom (physical or virtual) is a safe space, free of sexist, racist, homophobic, transphobic and discriminatory attitudes, either towards students or towards teachers. We trust that together we can create a safe space where we can make mistakes and learn without having to suffer prejudice from others.

Contents

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

Activities and evaluation system

Assessment:

  • PR_E: Individual written test. Weighting of the final grade 60% if the grade is> = 5
  • PRAC: internships from 1 to 4. Weighting in the final grade 40% (each 10%) if a minimum of two internships have been approved

Final grade calculation (NF):

  • Si PR_E >= 5 and 2 or more approved internships : NF = PON = PR_E 0,60 + PRAC 0,40 
  • If PR_E < 5 or not 2 approved practices: NF = min (PR_E, PON)

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:

  • Attendance at internships is mandatory. If a student does not attend an internship session he will be graded with a grade of 0 (zero) in the corresponding internship
  • Following UPF regulations, if it is detected that a practice or a written test has been copied from a classmate, the mark will be 0 (zero) for both what has been copied and what has been allowed to be copied.
  • In order for the student to be entitled to recovery he / she must have taken the written test

Use of Generative Artificial Intelligence:

The use of generative artificial intelligences (IAGs) must be limited to those aspects that are not fundamental in the context of the subject. They can be used, critically, as a mechanism to resolve doubts about the subject and/or to improve the writing of deliverable documents and/or as an aid in the generation of auxiliary code that is outside the scope of the subject topics. In the second case (improvement of the writing) the participation of IAG in the writing must be made explicit in the document. In the last case (code generation) it will be essential to mention its nature as “generated by IAG” by explaining the model used and the prompt supplied, even if it has been subsequently personalized and/or modified. IAGs may not be used to generate programming code, not even in the form of fragments, when this code is within the scope of the subject topics and/or is of an assessable nature. This prohibition remains even if the code is subsequently personalized and/or modified. If you have any doubts regarding the legitimacy or not of the use of IAGs, you must contact, a priori, the professor of the subject. 
 

Bibliography

Basic

Russel, Stuard and Norvic, Peter (2013), "Artificial Intelligence: a moderate approach". (3rd edition) Prentice Hall.