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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.
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.
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.
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
Assessment:
Final grade calculation (NF):
Recovery:
Normative:
Russel, Stuard and Norvic, Peter (2013), "Artificial Intelligence: a moderate approach". (3rd edition) Prentice Hall.