Academic year 2017/2018 Syllabus of previous years
Official course title ANALISI PREDITTIVA
Course code CT0429 (AF:212604 AR:97067)
Modality On campus classes
ECTS credits 6
Degree level Bachelor's Degree Programme
Educational sector code SECS-S/01
Period 1st Semester
Course year 3
Moodle Go to Moodle page
Course objectives
This course has the objective to provide the essential elements to build, test and validate predictive statistical models for complex data sets.
Basic elements of statistical inference.
Simple and Multiple Linear Regreession Models.
Predictive Accurcy: Resampling methods of Cross-validation and Bootstrap.
Hig dimensional data and large data sets (big data.
Non linear models.
Neural netwoks models.
Referral texts
James G, Witten D, Hastie T, Tibshirani R (2015). An Introduction to Statistical Learning. 6th version. Springer. Pagina web
Type of exam
Assessment methods
Several exercizes and homeworks concerning problems to be solved with the methods presented in the course will be assigned to the students. Students will then present their written solutions with possible discussion in the class.
Teaching methods
Presentation of real problems and possible solutions
Teaching language
  • Course with sustainable contents
  • Lecture notes, material for reference or for self-assessment available online or as e-book
  • Use of open-source software
Last update of the programme