The document provides an introduction to machine learning techniques for category representation, outlining topics like clustering, classification, dimensionality reduction, and density estimation. It discusses supervised, unsupervised, and semi-supervised learning approaches and how to evaluate models using techniques like cross-validation to avoid overfitting. The goal of the course is to introduce common machine learning algorithms used in object recognition systems.

1. Introduction to Machine Learning for Category Representation Jakob Verbeek November 27, 2009 Many slides adapted from S. Lazebnik

11. Example of classification Given: training images and their categories What are the categories of these test images?

24. Dimension reduction