Machine Learning

Argomenti del programma: In the first part, we will cover linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, subset selection and model regularization methods (ridge and lasso); tree-based methods, random forests and boosting. The focus is on the important elements of modern data analysis and its applications. The computing language is the R programming language.

Argomenti del programma: In the first part, we will cover linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, subset selection and model regularization methods (ridge and lasso); tree-based methods, random forests and boosting. The focus is on the important elements of modern data analysis and its applications. The computing language is the R programming language.

• Lecture notes, research papers and course material will be made available on the elearning platform.

Argomenti del programma: The course consists of methodological lectures complemented by practical lab sessions. In more detail, the course will cover the following topics: Supervised Learning: Classification. Unsupervised Learning: Clustering. Sentiment Analysis. Recommendations systems. Reinforcement Learning. Artificial Neural Networks. Convolutional Neural Networks.