Estudia

• CG3- Communicate in an effective way both orally and in writing, preparing documents and presenting projects and results with English language
• CG4- Designing creative proposals
• CB10- That students have the learning skills to allow them to continue studying in a way that will mostly be self-directed or autonomous
• CE1- Programming, at an advanced level, in the languages and libraries most used in intelligent field robotics
• CE2- Analyse a problem related to intelligent autonomous systems and identify the appropriate techniques and tools to solve it
• CE3- Understand, develop, modify and effectively apply machine learning methods.
• CE8- Understand the mathematical foundations of intelligent robotic system algorithms

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

Assessment activities:

Assessment tests conducted during the course will be assessed out of 10 total marks each.

Specific criteria for the "No show" grade:
Not making any of the assessment tests.

Single Assessment:
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.

Minimum requirements to pass:
A pass requires obtaining at least 5 marks, averaged over all tests conducted.

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.