||Machine learning deals with the automatic analisys of large scale data. Nowadays it conforms the basics of many computer vision methods, specially those related to visual pattern recognition or classification, where 'patterns' encompasses images of world objects, scenes and video sequences of human actions, to name a few. This module presents the foundations and most important techniques for classification of visual patterns, focusing on supervised methods. Also, related topics like image descriptors and dimensionality reduction are addressed. As much as possible, all these techniques are tried and assessed on a practical project concerning traffic sign detection and recognition, toghether with the standard metrics and procedures for performance evaluation like precision-recall curves and k-fold cross-validation. The learning outcomes are: (a) Distinguish the main types of ML techniques for computer vision: supervised vs. unsupervised, generative vs. discriminative, original feature space vs. feature vector kernelization. (b) Know the strong and weak points of the different methods, in part learned while solving a real pattern classification problem. (3) Being able to use existing method implementations and build them from scratch.