81610 - Machine Learning

Course Unit Page

  • Teacher Davide Maltoni

  • Credits 6

  • SSD ING-INF/05

  • Teaching Mode Traditional lectures

  • Language Italian

  • Course Timetable from Sep 22, 2016 to Dec 14, 2016

Academic Year 2016/2017

Learning outcomes

Providing the student with the concepts necessary: - to understand and apply machine learning approaches; - implement classification, regression and clustering algorithms to solve problems in different applicative fields; use neural networks and other deep learning techniques. 

Course contents

  • Artificial Intelligence and Machine Learning
  • Supervided and Unsupervised Learning
  • Classification and Regression
  • Classifiers: Bayes, k-Nearest Neighbor, Support Vector Machines, Multiclassifiers
  • Clustering (K-means, EM) and Dimensionality Reduction (PCA, DA)
  • Neural Networks (NN)
  • Introduction to Deep Learning
  • Convolutional Neural Networks (CNN)


Teacher's slides at:


Teaching methods

Lectures + Practical (guided) sessions in lab.

Lab assignments and solutions at:


Assessment methods

Written exam and/or home project

Teaching tools

Software libraries and tools for machine learning

Links to further information


Office hours

See the website of Davide Maltoni