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95866 - INTRODUCTION TO STATISTICAL LEARNING

Anno Accademico 2024/2025

                
                        Docente:
                        Paola Bortot
                    
                        Crediti formativi:
                        2
                    
                        SSD:
                        SECS-S/01
                    
                        Lingua di insegnamento:
                        Inglese
                    
                        Modalità didattica:
                        Convenzionale - Lezioni in presenza
                        
                            Campus:
                            Bologna
                        
                            Corso:
                            Laurea in
                            Economics and Finance (cod. 8835)

                            Risorse didattiche su Virtuale
                        
                                    Orario delle lezioni
                                
dal 10/02/2025 al 24/02/2025

Conoscenze e abilità da conseguire

The course aims at providing students with some of the main concepts and tools of Statistical Learning for economic and financial applications. These include tools for supervised learning, such as parametric and non-parametric regression and classification models, and resampling methods for model selection and assessment, such as cross-validation and bootstrap. The course emphasizes practical aspects by illustrating the methodology through empirical applications using the R software.

Contenuti

The following topics will be covered:

Introduction and Overview of Statistical Learning
Linear Regression as a Prediction Tool
Moving Beyond linear regression: K-Nearest Neighbors
Classification: K-Nearest Neighbors, Logistic Regression, misclassification rate, ROC curve, AUC.
Resampling Methods for Model Assessment and Selection: Bias-Variance trade-off; Training set, Validation set, Test set; Cross-Validation and the Bootstrap.

Testi/Bibliografia

James, Witten, Hastie and Tibshirani, An Introduction to Statistical Learning, Springer 2021 (second edition).

Lecture notes will be made available at the beginning of the course.

Metodi didattici

The emphasis of the course is on empirical applications. For this reason, for each topic, after introducing the relevant theory, illustrative real data examples will be given using the R language. Attending classes is highly recommended in order to better develop practical skills.

Modalità di verifica e valutazione dell'apprendimento

The final examination covers the contents of both Module 1 and Module 2 according to the format detailed on the Module 2 website

Strumenti a supporto della didattica

The statistical software R will be used to perform empirical studies. Additional resources include the software R Markdown for dynamic documentation and data repositories such as Kaggle, UCI Machine Learning and Google Finance.

Orario di ricevimento

Consulta il sito web di Paola Bortot