mala::home Davide “+mala” Eynard’s website

Machine Learning (2017-2018)

The objective of this course is to give an advanced presentation, i.e., a statistical perspective, of the techniques most used in artificial intelligence and machine learning for pattern recognition, knowledge discovery, and data analysis/modeling. The course will provide the basics of Regression, Classification, and Clustering with practical exercises using the R language.

This page is mainly devoted to the labs part of the course. If you want more information about the course please check the Machine Learning page on prof. Matteucci's website.


  • Dec 19, 2017: Material for lab09 and lab10 is online
  • Dec 5, 2017: Material for lab08 is online
  • Nov 28, 2017: Material for lab07 is online
  • Nov 21, 2017: Material for lab06 is online
  • Nov 08, 2017: Material for lab04+lab05 is online
  • Oct 10, 2017: Material for lab03 is online
  • Oct 03, 2017: Material for lab02 is online
  • Sep 27, 2017: Material for lab01 is online
  • Sep 26, 2017: The new labs started. Enjoy!


  • R for Matlab users
  • Lab 1: Introduction to R (material + links)
  • Lab 2: Statistical Decision Theory (notes)
  • Lab 3: Linear regression basics (notes)
  • Lab 4: Multiple + advanced linear regression (notes)
  • Lab 5: Classification (Logistic regression) (notes)
  • Lab 6: Shrinkage methods (ridge/lasso) for regression and classification (notes, code)
  • Lab 7: Classification (LDA, QDA, KNN) (notes)
  • Lab 8: Introduction to Clustering, K-Means, Hierarchical (slides)
  • Lab 9: Clustering: advanced algorithms (slides1, slides2)
  • Lab 10: Clustering: evaluation (slides)