Estudia

• CG3- Comunicar-se de manera efectiva oralment i per escrit preparant documents i exposant projectes i resultats amb llengua anglesa
• CG4- Dissenyar propostes creatives
• CB10- Que els estudiants tinguin les habilitats d'aprenentatge que els permetin continuar estudiant d'una manera que haurà de ser en gran manera autodirigida o autònoma
• CE1- Programar, a nivell avançat, en els llenguatges i llibreries més utilitzats en la robòtica de camp intel·ligent
• CE2- Analitzar un problema relacionat amb sistemes autònoms intel·ligents i identificar les tècniques i les eines apropiades per resoldre'l
• CE3- Entendre, desenvolupar, modificar i aplicar de manera efectiva mètodes d'aprenentatge automàtic
• CE8- Comprendre els fonaments matemàtics dels algorismes utilitzats en els sistemes robòtics intel·ligents

1. Overview of basic statistics and probability

          1.1. Getting started with R

          1.2. Basic concepts and data exploration

          1.3. Probability distributions

          1.4. Sampling, estimation and hypothesis testing

2. Introduction to multivariate data analysis

          2.1. Multivariate data

          2.2. Data reduction: principal components analysis and biplot

          2.3. Supervised classification: discriminant analysis

          2.4. Resampling and cross-validation

          2.5. Correspondence analysis of count data

          2.6. Low-dimensional visualisation: multidimensional scaling

3. Statistical modelling

          3.1. Linear and generalised linear regression

          3.2. Logistic regression for binary response

          3.3. Poisson regression for counts

          3.4. Additive models based on smooth splines

          3.5. Model assessment and simplification

          3.6. Regression analysis with many variables

• Faraway, J.J. (2016). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Segona). Chapman and Hall/CRC.
• Hastie, T., Tibshirani, R. and Friedman, J.H. (2016). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Segona). Springer.
• James, G., Witten, D., Hastie, T. and Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R (Segona). Springer.
• Johnson, R.A., Wichern, D.W. (2007). Applied Multivariate Statistical Analysis (Sisena). Pearson Prentice Hall.
• Schumacker, Randall E. (2016). Using R with multivariate statistics. Thousand Oaks, California: SAGE Publications, Inc.. Catàleg

Activitats d'avaluació:

Each assessment task conducted during the course will be granted between 0 and 10 marks.

Criteris específics de la nota «No Presentat»:
Not making any of the assessment tests.

Avaluació única:
To be discussed with the teacher at the start of the course. It would involve to pass a test in relation to the contents of the course.

Requisits mínims per aprovar:
A pass requires obtaining at least 5 marks, after applying the corresponding weights and averaging over all the assessment tasks.

On request.

All communications and notices will be posted on the Moodle's noticeboard for the course.

The teacher can be reached in person during the sessions or by e-mail.

The programs R (https://www.r-project.org/) and RStudio (https://www.rstudio.com/products/rstudio/download/) as expected to be installed on personal computers at the start of the course.

All required course material, communications and notices will be found at the Moodle page of the course.

The use of AI tools is not allowed for any assessment task.