- Generic: 1.1. Basic general knowledge 1.2. Ability to analyse and synthesise 1.3. Ability to apply knowledge in practice 1.4. problem solving 1.5. Ability to integrate knowledge - Cognitive: 2.1. Knowledge of the typology of astronomical data. 2.2. Understanding of the nature of data according to instrumentation. 2.3. Statistical processing of time series. 2.4. Image processing 2.5. Bayesian inference, inversion problem, parametric modelling. 2.6. Non-parametric modelling. 2.7. Classification problems. - Procedural/Instrumentation 2.8. Use of software for data modelling. 2.9. Use of machine learning methods for time series modelling and prediction. 2.10. Deep learning for classification, neural networks, supervised and unsupervised learning.

Unit 1. Model parameter estimation. Parametric models. Time series in Astrophysics. Light curves, radial velocity curves and gravitational waves. Bayesian inference: MCMC and Importance sampling. Unit 2. Model selection. Cosmological model. Flare oscillations. Number of exoplanets. Hypothesis testing, Bayesian evidence, Nested Sampling. Unit 3. Gaussian processes for non-parametric fitting. Emission and absorption lines. Light curves of eclipsing binaries. Transiting exoplanets. Unit 4. Machine learning tools. Astronomical images, time series and spectral classification. 1D and 2D neural networks. Clustering. K-means. Gaussian mixture models. Unit 5. Other deep learning tools. Pattern detection/recognition in time series. Unsupervised learning.

The course will be taught in two types of classes: theory and computational practice. THEORY (2 ECTS) The sessions explain the basic fundamentals and analysis tools corresponding to the core of the course. Numerous examples of real data, their properties and behaviour will be provided. For this purpose, a blackboard and audiovisual media (slides, video, ...) will be used. Machine and deep learning methods appropriate to the course will be presented. PRACTICALS (1 ECTS) For the practical classes, real computer data analysis exercises will be proposed. These exercises will be guided by the teacher of the course and will be carried out in class.