First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SC01122905, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
SS1736, Degree course structure A.Y. 2014/15, A.Y. 2018/19
bring this page
with you
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination MACHINE LEARNING
Website of the academic structure
Department of reference Department of Statistical Sciences
E-Learning website
Mandatory attendance No
Language of instruction Italian
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge FABIO AIOLLI INF/01

Course unit code Course unit name Teacher in charge Degree course code

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines INF/01 Computer Science 6.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Laboratory 1.0 8 17.0 No turn
Lecture 5.0 40 85.0 No turn

Start of activities 01/10/2018
End of activities 18/01/2019
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
8 a.a. 2018/2019 01/10/2018 28/02/2020 AIOLLI FABIO (Presidente)
BALDAN PAOLO (Supplente)
7 a.a. 2017/2018 01/10/2017 28/02/2019 AIOLLI FABIO (Presidente)
BALDAN PAOLO (Membro Effettivo)
TOLOMEI GABRIELE (Membro Effettivo)

Prerequisites: The student should be familiar with basic concepts in Probability and Analysis of multivariate functions. It is also advisable to have basic knowledge of Programming and Artificial Intelligence.
The course does not have prerequisites.
Target skills and knowledge: The aim of the course is to introduce the student to the basic concepts that characterize Machine Learning, i.e. the class of techniques and algorithms that starting from empirical data allow a computer system to aquire new knowledge, or to correct and/or to refine knowledge already available. These techniques are particularly useful for problems for which it is impossible or very difficult to reach a mathematical formalization usable for the definition of an ad hoc algorithmic solution. Examples of these problems are perceptual tasks, such as visual recognition of handwritten digits, or problems in which data is corrupted by noise or is incomplete. The course mainly covers numerical methods.
Students will face practical exercises in a computer lab that allow them to test the application of the acquired knowledge to small practical examples.
Examination methods: The student must pass a written examination and, if deemed necessary by the teacher, an oral examination.
Assessment criteria: The text of the written exam includes some questions that aim to assess the level of learning reached by the student concerning the concepts taught in the course and the student's ability to perform critical analysis on them. The text also includes questions in which the student is required to show understanding of the applicative issues taught in the computer lab. These questions are designed to assess whether the student has developed the ability to apply the concepts learned during the course.
In the event that the assessment of the written exam is not satisfactory for the student, the teacher may supplement the written examination with an oral examination to better assess the level of learning of the student.
Course unit contents: The course will cover the topics listed below
- Introduction:
When to apply Machine Learning techniques; Machine Learning Paradigms; Basic ingredients of Machine Learning.
- Learning Concepts:
Complexity of the Hypothesis Space; Complexity Measures; Examples of Supervised Learning Algorithms;
- Decision Trees:
Learning Decision Trees; Treatment of Numerical Data, Missing Data, Costs; Pruning Techniques and Derivation of Decision Rules.
- Probabilistic Learning:
Bayesian Learning; Examples of Application to Supervised and Unsupervised Learning (clustering); Optimal Bayes classifier; EM.
- Neural Networks and Support Vector Machines:
Introduction to Neural Networks; Classification Margin, Support Vector Machines for Classification and Regression, Kernel Functions.
- Application Issues:
Classification Pipeline, Representation and Selection of Categorical Variables; Model Selection, Holdout, CrossValidation, LeaveOneOut CV; Exterior and Interior Criteria for Evaluating a Clustering System; Recommendation Systems: Types, Approaches, Evaluation Measures.
Planned learning activities and teaching methods: The course consists of lectures and exercises in the computer lab. The exercises in the computer lab allow the students to experiment, under various operating scenarios, with the techniques introduced in class. In this way, students can verify experimentally the concepts learned in class and acquire the ability to apply the learned concepts and to perform critical judgment.
Additional notes about suggested reading: Slides presented during the lectures are made ‚Äč‚Äčavailable to students as reference material.
Textbooks (and optional supplementary readings)
  • Mitchell, Tom M., Machine learning. New York: McGraw-Hill, 1998. Cerca nel catalogo
  • Alpaydin, Ethem, Introduction to machine learning. Cambridge: The MIT press, 2010. Cerca nel catalogo