INTRODUCTION TO MACHINE LEARNING

Teachers: 
MORANDIN Francesco
Credits: 
6
Year of erogation: 
2021/2022
Unit Coordinator: 
Disciplinary Sector: 
Probability and Mathematical Statistics
Semester: 
Second semester
Year of study: 
1
Language of instruction: 

Italian

Learning outcomes of the course unit

Each student should obtain the knowledge and the practical ability to develop advanced statistical analysis, based on the treated methods and techniques.
It is moreover required to become able to use Microsoft Excel in a thorough, flexible and very fast way.

Prerequisites

Analisi matematica 2, geometria 1, elementi di probabilità

Course contents summary

1. Multiple regression, linear and polynomial, also heteroscedastic.
2. Design of Experiments (DoE).
3. Analysis of variance (ANOVA), one-way, two-way and with interactions.
4. Pearson's chi-square test (goodness-of-fit test and contingency tables).
5. Advanced hypothesis tests: Operating Characteristic (OC) Curves and power; test for p with exact binomial law. Fisher-Irwin test.
6. Montecarlo simulation.

Recommended readings

1. Sheldon M. Ross - Introduction to Probability and Statistics for Engineers and Scientists, Fourth Edition - Academic Press, 2009 - ISBN 978-0123704832
2. Richard A. Johnson, Dean W. Wichern - Applied Multivariate Statistical Analysis - Pearson, 2007 - ISBN 9780131877153
3. Michael R. Middleton - Data Analysis Using Microsoft Excel: Updated for Office XP - South-Western College Pub, 2003 - ISBN 978-0534402938
4. Andrew Sleeper - Design for Six Sigma Statistics - McGraw-Hill, 2005 - ISBN 978-0071451628

Teaching methods

Class teaching (32 h) and computer lab on Microsoft Excel 2010 (24 h)

Assessment methods and criteria

1. Practical test: multivariate statistical analysis on MS Excel.
2. Interview.