Laurea Magistrale (M.Sc.) in Stochastics and Data Science

Statistical machine learning

Statistical machine learning

Academic year 2016/2017

Sommario del corso

• Course objectives
• Results of learning outcomes
• Course delivery
• Learning assessment methods
• Program
• Suggested textbooks and readings
• Notes
• Teaching tools

Course objectives

The course introduces methods and models to extract important patterns and trends from big amount of data, and presents basic concepts of machine learning and data mining from a statistical perspective. All the methods will be introduced from a theoretical point of view and implemented on real datasets in the R language.

Results of learning outcomes

Knowledge and understanding 

• Advances knowledge of parametric and nonparametric models for prediction and classification

Applying knowledge and understanding

• Ability to convert various problems and data into statistical models to perform several type of  prediction/classification.

Making judgements

• Students will be able to discern the different aspects of statistical learning in modern settings.

Communication skills

• Students will properly use statistical language to comunicate the results of their findings.

Learning skills

• The skills acquired will give students the opportunity of improving and deepening their knowledge of statistical modeling.

Course delivery

Half of the lectures are devoted to the theorerical aspects of statistical machine learning and the remaining half to their practical implemetation in the R software considering both the related numerical and computational issues. Exercises will be assigned during  lectures and lab sessions. 

Learning assessment methods

The exam consists of three parts: the first part is a written exam on theory; the second part is a practical session with R; the last part is an oral discussion.

Program

Introduction

• Context and motivations;
• Trade-off between goodness-of-fit and model complexity (i.e. variance and bias);
• Model selection techiniques (AIC, BIC, cross validation);
• Training and test set; 

Regression

• Variable selection and shrinkage
• Elements of nonparametric regression
• Structured nonparametric regression

Classification:

• Logistic and multilogit regression;
• Elements of nonparametric classification
• Ensable techniques (bagging, boosting, random forest);

Miscellanea:

• Tools for data visualization;
• Computational tools (parallel computing, recursive estimations);

• AZZALINI, SCARPA. Data analysis and data mining . Oxford University Press
• HASTIE, TIBSHIRANI AND FRIEDMAN. The elements of statistical learning: data mining, inference and prediction. Springer-Verlag.

Class schedule

Note

This course will be delivered at the ESOMAS Department.

• Teaching materials
• Exams and finals
• Go to Moodle
• Visit the forums