Teacher

cotroneo rossana
(syllabus)
1. Data: structure and manipulation • Statistical data • The IT data • Data manipulation: random and systematic errors; missing data; outlier; logical inconsistencies data; anomalous data; imputation technique for missing data.
2. The syntheses of the data, their transformations 3. multivariate distributions: • multinormal distribution • multinomial distribution
4. Unsupervised classification (cluster analysis)  Nonhierarchical methods: Kmeans, Pam and fuzzy Kmadoidi  variables Selection, best selection of the number of clusters,  The aggregative hierarchical methods
5. Data reduction methodologies: • principal component analysis (PCA); • factor analysis • correspondence analysis and multiple correspondence analysis
6. Unsupervised classification (cluster techniques)
7. Supervised classification (cluster techniques)
o Linear regression; o Generalized linear model o Logistic regression; o Classification trees o Support vector machine o Random forest o Gradient boosting o Neural networks
• Traning, Evaluating and Testing model
(reference books)
 slide delle lezioni che saranno caricate sulla piattaforma Moodle
