- Good knowledge of the fundamentals of Statistics (i.e. descriptive statistics, probability, inferential statistics, linear regression model).
- Basic knowledge of the R programming language.
The course consists in theory lectures for a total of 48 hours. Extra hours (usually 16) are dedicated to R lab sessions. The lectures/labs calendar will be published at the beginning of the course on the Moodle page of the course.
- Introduction to machine learning: supervised versus unsupervised learning, the bias-variance trade-off. - Classification methods: K-nearest neighbors classification, logistic regression, naive Bayes, linear and quadratic discriminant analysis, classification trees (including bagging, random forests, boosting). - Regression methods: K-nearest neighbors regression, regression trees (including bagging, random forests, boosting), non-linear regression models (splines, GAM). - Resampling methods: cross-validation and bootstrap. - Unsupervised learning methods: clustering, principal component analysis.
