First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP7079228, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
SC2377, Degree course structure A.Y. 2017/18, A.Y. 2019/20
bring this page
with you
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination STATISTICAL LEARNING 2 (MOD. B)
Website of the academic structure
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction English

Teacher in charge ALBERTO ROVERATO SECS-S/01
Other lecturers BRUNO SCARPA SECS-S/01

Integrated course for this unit
Course unit code Course unit name Teacher in charge

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses SECS-S/01 Statistics 6.0

Course unit organization
Period Annual
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 6.0 48 102.0 No turn

Start of activities 30/09/2019
End of activities 20/06/2020
Show course schedule 2019/20 Reg.2017 course timetable

Examination board
Examination board not defined


Common characteristics of the Integrated Course unit

Prerequisites: basic probability theory; multivariable calculus; linear algebra; basic computing skills
Target skills and knowledge: become familiar with statistical thinking; gain adequate proficiency in the development and use of standard statistical inference tools; be able to analyse datasets using a modern programming language such as R
Examination methods: written test for mod-A
project work and oral exam for mod-B
Assessment criteria: the successful student should show knowledge of the key concepts, skills in the analysis of data and competency in applications

Specific characteristics of the Module

Course unit contents: Part 2
- Models : normal linear models; inference for linear models; generalized linear models; inference for generalized linear models
- Model selection
- Multivariate Analysis: dimension reduction; classification; clustering
Planned learning activities and teaching methods: Lectures and Laboratories
Additional notes about suggested reading: Applications can be found in:
- Nolan, D.A. & Speed, T. (2000). Stat Labs: Mathematical Statistics Through Applications. Springer.
- Torgo, L. (2011). Data Mining with R: Learning with Case Studies. Chapman & Hall/CRC.

Methods for specific fields of applications can be found in the following books:
-Campbell, R.C. (1989). Statistics for Biologists (3rd ed.). Cambridge University Press.
-Devore, J.L. (2000). Probability and Statistics for Engineering and the Sciences (5th ed.). Duxbury Press, Pacific Grove, CA.
-Agresti, A. & Finlay. B. (2007). Statistical Methods for the Social Sciences (4th ed.). Prentice Hall
Textbooks (and optional supplementary readings)
  • Hastie, Trevor J.; Tibshirani, Robert, The elements of statistical learningdata mining, inference, and prediction. New York: Springer, 2009. Cerca nel catalogo
  • Lavine, M., Introduction to Statistical Thought. --: None, 2013.
  • Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning. --: Springer, 2013. Cerca nel catalogo