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  1. Courses

0010A6 - ADVANCED TOPICS IN STATISTICAL LEARNING

courses
ID:
0010A6
Duration (hours):
1
CFU:
6
SSD:
STATISTICA
Located in:
PESCARA
Url:
Course Details:
ECONOMICS AND BUSINESS ANALYTICS/CORSO GENERICO Year: 1
Year:
2025
Course Catalogue:
https://unich.coursecatalogue.cineca.it/af/2025?co...
  • Overview
  • Syllabus
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Overview

Date/time interval

Secondo Semestre (11/02/2026 - 18/05/2026)

Syllabus

Course Objectives

The meetings provides knowledge about the analysis of statistical data focusing on statistical learning (also non-linear) and object data analysis methodologies both in a predictive and non-supervised context (supervised and non-supervised learning).

LEARNING OUTCOMES
The meetings aim at completing student's training with notions and tools useful to deepen the aspects of advanced statistical analysis. The training will then be completed and enriched by the following skills:

Knowledge and understanding / Applying knowledge and understanding

- Knowledge of statistical concepts for multivariate analysis (including non-linear tecniques)

- Ability to apply the principles of statistical reasoning in the preparation and interpretation of statistical papers
- Ability to use R software for advanced statistical analysis

Making judgements
- To learn the logical and statistical concepts that are indispensable for working independently in the research, selection and processing of
company data

Communication skills
- Learn the terminology and statistical techniques of multivariate analysis and object data analysis to communicate or correctly discuss the results of the analysis

Course Prerequisites

Mathematics (calculus), linear algebra and statistical inference (estimation and statistical test), Data Mining

Teaching Methods

Weekly meetings for the development of a project using advanced statistical tecniques for the analysis of complex data

Assessment Methods

The exam consists in discussing (60 minutes) the project based on the statistical analysis of data sets chosen together with the teacher. The final grade will be given in thirtieths.

Texts

Kevin Murphy (2012) Machine learning : a probabilistic perspective, The MIT Press, Cambridge, Massachusetts, London, England

Contents

The following topics are considered as important parts of the teaching program for the fulfilment of the objectives: data visualization of complex data, non-parametric regression, dependence analysis of complex data (non-linear), text mining, Object data analysis, Shape analysis

Course Language


Meetings with students will be in Italian. Suggested textbooks will be in English

More information

E-mail: ippoliti@unich.it

Students will be received on Mon and Wed between 15:00 and 16:00; Appointments can be fixed by e-mail.

Degrees

Degrees

ECONOMICS AND BUSINESS ANALYTICS 
Master’s Degree
2 years
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People

People

IPPOLITI Luigi
Gruppo 13/STAT-01 - STATISTICA
AREA MIN. 13 - Scienze economiche e statistiche
Settore STAT-01/A - Statistica
Docenti di ruolo di Ia fascia
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