First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
Course unit
MACHINE LEARNING (Numerosita' canale 2)
INP6075419, A.A. 2017/18

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
IN0521, Degree course structure A.Y. 2009/10, A.Y. 2017/18
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination MACHINE LEARNING
Department of reference Department of Information Engineering
E-Learning website
Mandatory attendance No
Language of instruction English
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 VANDIN

Course unit code Course unit name Teacher in charge Degree course code
INP6075419 MACHINE LEARNING (Numerosita' canale 2) FABIO VANDIN IN0527
INP6075419 MACHINE LEARNING (Numerosita' canale 2) FABIO VANDIN IN0524
INP6075419 MACHINE LEARNING (Numerosita' canale 2) FABIO VANDIN IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines ING-INF/04 Automatics 3.0
Core courses ING-INF/05 Data Processing Systems 3.0

Mode of delivery (when and how)
Period First semester
Year 1st Year
Teaching method frontal

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

Start of activities 25/09/2017
End of activities 19/01/2018

Examination board
Board From To Members of the board
1 A.A. 2016/2017 01/10/2016 15/03/2018 CHIUSO ALESSANDRO (Presidente)
VANDIN FABIO (Membro Effettivo)
ZORZI MATTIA (Supplente)

Prerequisites: Basic Knowledge of Probability Theory, Statistics and Linear Algebra
Target skills and knowledge: The aim of this course is to provide the fundamentals and basic principles of the learning problem as well as to introduce the most common algorithms for regression and classification. Both supervised as well as unsupervised learning will be covered, with possibly a brief outlook into more advanced and modern topics such as sparsity and boosting. The course will be complemented by hands-on experience through computer simulations.
Examination methods: Written test and take-home computer simulations.
Assessment criteria: Knowledge of the basic tools for prediction (regression and classification). Analytical and practical ability in the use of these tools for the solution of basic problems.
Course unit contents: Motivation; components of the learning problem and applications of Machine Learning. Supervised and unsupervised learning.

PART I: Supervised Learning

Introduction: Data – Classes of models - Losses
Probabilistic models and assumptions on the data
Models, Losses and the regression function. Regression and Classification
When is a model good? Model complexity, bias variance tradeoff/generalization (VC dimension – generalization error)

Least Squares, Maximum Likelihood and Posteriors.

Models for Regression
Linear Regression (scalar and multivariate) – Stein’s paradox and Regularization – Subset Selection
Linear-in-the-parameters models, Regularization.
Local and global models (Smoothing Kernels and NNR)
Dimensionality Reduction: Principal Component Regression, Partial Least Squares.
Classes of non linear models: Sigmoids, Neural Networks
Kernel Methods: SVM

Models for Classification

Linear Discriminant Analysis, Logistic Regression, NN, Perceptron, Naïve Bayes Classifier, SVM

Validation and Model Selection
Generalization Error, Bias-Variance Tradeoff, Cross Validation, SURE. Model complexity determination

PART II: Unsupervised learning

Cluster analysis: K-means Clustering, Mixtures of Gaussians and the EM estimation
Dimensionality reduction: Principal Component Analysis (PCA)
Planned learning activities and teaching methods: Theoretical classes and problem solving sessions. Computer Simulations (in the lab).
Additional notes about suggested reading: The course will be based on the four textbooks: "Machine Learninga probabilistic perspective", "Pattern Recognition and Machine Learning", "The Elements of Statistical Learning", “Understanding Machine Learning: from Theory to Algorithms” (see Section "Testi di Riferimento").
Textbooks (and optional supplementary readings)
  • T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning.. --: Springer, 2008. Cerca nel catalogo
  • C. M. Bishop, Pattern Recognition and Machine Learning. --: Springer, 2006. Cerca nel catalogo
  • Shalev-Shwartz, Shai; Ben-David, Shai, Understanding machine learningfrom theory to algorithms.. --: Cambridge University Press, 2014. Cerca nel catalogo
  • Murphy, Kevin P., Machine Learninga probabilistic perspective. --: Mit press, 2012. Cerca nel catalogo