Machine Learning (2016-2017)
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
- Feb 3, 2017: Homework Part 3 and 4 are online.
- Feb 1, 2017: Homework Part 2 is online.
- Jan 19, 2017: Homework Part 1 is online.
- Dec 23, 2016: Material for Lab10 is online.
- Dec 20, 2016: Material for Lab09 is online.
- Dec 13, 2016: Material for Lab08 is online.
- Dec 01, 2016: Material for Lab07 is online.
- Nov 15, 2016: Material for Lab06 is online.
- Oct 21, 2016: Material for Lab04 and Lab05 is online.
- Oct 18, 2016: Material for Lab03 is online.
- Oct 14, 2016: Material for Lab02 is online.
- Oct 11, 2016: Material for Lab01 is online.
- Oct 07, 2016: The new labs started. Enjoy!
Material
- R for Matlab users
- Lab 1: Introduction to R (material + links)
- Lab 2: Introduction to Clustering and K-Means(slides, handouts)
- Lab 3: Clustering: K-Medoids, Fuzzy C-Means, and Hierarchical (slides, handouts)
- Lab 4: Clustering: GMM, DBSCAN, and Jarvis-Patrick (material, slides, handouts). Additional material on Approximate Nearest Neighbors search
- Lab 5: Clustering: spectral clustering (introduction, multimodal), evaluation (material, slides, handouts)
- Lab 6: Statistical Decision Theory (material, R code)
- Lab 7: Linear Regression Basics (material, R code, t-distribution table)
- Lab 8: Multiple / Advanced Linear Regression (material)
- Lab 9: Classification (Logistic Regression) (material)
- Lab 10: Classification (LDA, QDA, KNN) (material)
Tools
- Clustering demo 2.0.2 by Markus Maier