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.

News

  • 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!

Material

  • 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)

Tools