Prof. Laura Anderlucci

Argomenti del programma: Descriptive Statistics Basic concepts. Frequency distributions. Describing data by graphs. Descriptive summary statistics: central tendency and variability measures and their properties. Association in contingency tables: distributional and mean dependence. Simple linear regression and correlation. Probability Basic concepts and Bayes theorem . Random variables. Central Limit theorem.

Argomenti del programma: Part I: Introduction to Statistical Learning Part II: Data Visualization and Reporting Part III: Supervised Learning Cross-Validation Naïve Bayes Logistic Regression; k -Nearest Neighbors; Nearest Shrunken Centroid; Regression and classification trees; Introduction to he Bootstrap; Bagging; Random Forests; Boosting.

Argomenti del programma: Part 0: Introduction to Supervised Statistical Learning Part 1: Resampling methods Cross-Validation Part 2: Classification Naive Bayes k-Nearest Neighbours Logistic Regression Linear Discriminant Analysis Part 3: Dimension Reduction and Regularisation Part 4: Tree-based methods Regression and Classification trees Bagging; Random Forests; Boosting Part 5: Support Vector Machines