Estudia

• CB6 Tenir i comprendre coneixements que aportin una base o oportunitat de ser originals en el desenvolupament i/o aplicació d'idees, sovint en un context de recerca.
• CB7 Que els estudiants sàpiguen aplicar els coneixements adquirits i la seva capacitat de resolució de problemes en entorns nous o poc coneguts dins de contextos més amplis relacionats amb la seva àrea d'estudi
• CB9 Que els estudiants sàpiguen comunicar les seves conclusions i els coneixements i raons últimes que les sustenten a públics especialitzats i no especialitzats d'una manera clara i sense ambigüitats
• CB10 Que els estudiants tinguin les habilitats d'aprenentatge que els permetin continuar estudiant d'una manera que haurà de ser en gran mesura autodirigida o autònoma
• CG5 Treballar en equips multidisciplinars, establint aquelles relacions que més poden ajudar a fer sortir potencialitats de cooperacio´
• CE1 Programar a un nivell avançat en els llenguatges i llibreries més utilitzades en la ciència de dades
• CE6 Analitzar un problema de ciència de dades i identificar les tècniques i les eines apropiades per a resoldre'l
• CE7 Entendre, desenvolupar, modificar i aplicar de manera efectiva mètodes d'aprenentatge automàtic
• CE8 Quantificar la bondat dels resultats obtinguts mitjançant la tecnologia de la ciència de dades a través de mètriques adequades
• CE13 Entendre les dades a partir de visualitzacions adequades

1. Introduction and Machine Learning concepts

2. Unsupervised learning. Introduction. Data clustering. Density estimation. Dimensionality reduction. Topological representation. Association rule learning. Sequence learning.

3. Supervised learning. Introducton. Linear and logistic regression, k-nearest neighbours, support vector machines, decision trees and ensemble methods, neural networks, xarxes Bayesianes. Semi-supervised learning. Algorithm selection. Explainability.

• Russell, Stuart J. (2022). Artificial intelligence : a modern approach (Fourth Edition, Global Edition). Harlow: Pearson Education Limited. Catàleg
• Andriy Burkov (2019). The hundred-page machine learning book. Andriy Burkov.
• Hastie, Trevor. (2009). The Elements of statistical learning : (2nd ed.). New York [etc.]: Springer. Catàleg
• Gareth James , Daniela Witten , Trevor Hastie , Robert Tibshirani (2021). An Introduction to Statistical Learning with Applications in R (2021). Springer Nature Link.
• Murphy, Kevin P. (2012). Machine learning : a probabilistic perspective. Cambridge, Mass: MIT Press. Catàleg
• Kevin P. Murphy (2022). Probabilistic machine learning : an introduction. Cambridge, Massachusetts: The MIT Press.
• Kevin P. Murphy (2023). Probabilistic machine learning : Advanced topics. Cambridge, Massachusetts: The MIT Press .

Activitats d'avaluació:

The course's final grade is calculated as the mean of the grades of the evaluation activities, weighted according to the percentages provided in the table above.

The instructors will indicate the allowed uses of generative AI technologies in each evaluation activity separately. If permitted, it is mandatory to clearly indicate which tools were used and how. A lack of transparency in this regard may result in failing the corresponding activity.


The student will access the classroom where the assessment activity is carried out with all communication devices (mobile phones, computers, tablets, smart watches, etc.) OFF and in backpacks/bags. If someone is found not to comply with this rule, a grade of 0 will be assigned to the activity, and the disciplinary actions described in Article 21 of the Regulations governing the assessment and grading processes of students at UdG will be imposed.
If, during the correction of the activity, the existence of possible fraud is determined, the teacher reserves the right to validate the grade using the assessment methodology of their convenience.
Any other detected act of academic fraud (impersonation, plagiarism, copying, the purchase or sale of academic work, etc.) will follow the same procedure.

Criteris específics de la nota «No Presentat»:
Completing any evaluation activity during the course will result in a numeric grade. Otherwise, an NP grade will be issued.

Avaluació única:
To apply for a single assessment of the whole course, a formal request must be made to the Academic Secretary.

The single assessment consists of a single exam where the contents of the entire course will be assessed. The qualification of this exam will represent the final grade.

Requisits mínims per aprovar:
To pass the course, a minimum grade of 5.0 must be obtained.

Office hours can be arranged by request. Contact the professor by email to agree on the details.

During the course sessions, the students could ask questions according to the protocol established by the professor (raise your hand).

Moodle's forums are encouraged as the main means for doubt and question answering so that it is accessible to all the students.

Personal inquiries will preferably be answered by email.

The recommended bibliography is complemented by material prepared by the instructors, which will be made available progressively on Moodle as the course advances.