Machine Learning (2018-2019)
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 11, 2018: Material for lab09 and lab10 is online
- Dec 3, 2018: Material for lab08 is online
- Nov 21, 2018: Material for lab05, lab06, and lab07 is online
- Nov 13, 2018: Material for lab04 is online
- Oct 21, 2018: Material for lab03 is online
- Oct 2, 2018: Material for lab01 and lab02 is online
- Oct 1, 2018: The new labs started. Enjoy!
Material
- R for Matlab users
- Lab 1: Introduction to R (material + links)
- Lab 2: Statistical Decision Theory (notes, bias-variance formulation, bias-variance MATLAB demo)
- Lab 3: Linear regression basics (notes, t-distribution table, inference for regression equations)
- Lab 4: Multiple + advanced linear regression (notes)
- Lab 5: Classification (Logistic regression) (notes)
- Lab 6: Classification (LDA, QDA, KNN) (notes)
- Lab 7: Shrinkage methods (ridge/lasso) for regression and classification (notes, code)
- Lab 8: Introduction to Clustering, K-Means, Hierarchical (slides)
- Lab 9: Clustering: advanced algorithms (slides1, slides2)
- Lab 10: Clustering: evaluation (slides)
Tools
- Clustering demo 2.0.2 by Markus Maier