Machine Learning

  • 02/10/2018: Starting date
  • 11/10/2018: Class on Tuesday 16/10/2018 will not take place
  • 18/10/2018: This morning 18/10/2018 class will not take place
  • 11/10/2018: Classes on Wednesdays are moved to Thursdays from 9:00 to 11:00 - Polo Lodovici, Room Lab2
  • Slides form classes 3,4,5,6 and 7 have been added
  • 14/11/2018 Class on 15/11/2018 will not take place. Next lesson TUESDAY 20/11/2018
  • 22/11/2018 Class on 27/11/2018 and 29/11/2018 will not take place.
  • 17/12/2018 Class on 18/12/2018 will not take place due to the persistence of bad weather conditions.
  • 18/12/2018 Class of 18/12/2018 will be recovered tomorrow 19/12/2018 from 14:00 to 17:00.


ESSE3 Link

Class schedule:

  • Tuesday 11:00 - 13:00 - AB1
  • Thursday 9:00 - 11:00 - LAB2

Students Office hours:

  • Thursday from 11:00 to 13:00


The aim of the course is to provide the student with knowledge and skills in the area of machine learning.
At the end of the course the student should be able to:

  • distinguish the various machine learning paradigms;
  • know the learning theory
  • know classification algorithms, regression, clustering and dimension reduction;

After completing the course, the student must demonstrate that he is able to:

  • apply the different machine learning paradigms
  • implement the classification, regression, clustering and dimensionality reduction algorithms;
  • design and implement systems able to learn automatically from real data and situations;

At the end of this training activity, the student will be able to express himself clearly and with appropriate terms, using the English language, in the learning discussions as well as expose the results of a research concerning technical aspects of machine learning.

LEARNING SKILLS At the end of this training activity the student will be able to:

  • Finding and learning the innumerable algorithms and techniques that are presented in the field of machine learning
  • Implementing and using the new algorithms

  • Probabilistic learning theory and the “learning problem”
  • The VC-dimension (Proof of the maximum margin)
  • Notions of Probability and Linear Algebra
  • Linear Models and Regression for Classification
  • Dimensionality Reduction
  • Clustering
  • Neural networks (an introduction)
  • Decision Tree, Random Forest, Ensemble
  • Balanced and unbalanced data
  • Evaluation Metrics (AUC, ROC, Confusion Matrix…)
  • Examples of coding in Python

Course Slides

Reference books

  • C.M. Bishop, Pattern Recognition and Machine Learning, Springer - 2006
  • D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press. - 2012 Online version
  • T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer - 2008 Online version

Other sources Some of the material is taken from the ML Course of Caltech given by Prof. Yaser Abu Mostafa and his collaborators

Additional Material

Exam Dates A.Y. 2015/2016

  • Winter session dates here
  • Summer session dates here
  • Autumn session dates here
  • Winter session dates here (2016)

Exam rules: The exam consists in two sessions: a written session and an oral one. The oral session is reserved for people who pass the written part with a mark greater or equal to 18.

Exam Results

  • N/A


  • Learning Probabilistic Theory
  • Learning paradigms
  • Classification
  • Clustering
  • Neural Networks
  • Support Vector Machines
  • Practical issues for ML