Programming (C, C++, Python, Matlab, etc...).

The main goal of this course is to introduce the main techniques and applications of machine learning in robotics. We will study the main areas in which machine learning is employed nowadays, paying also attention to recent approaches such as Deep Learning.

1. Introduction - Why is Machine Learning useful in robotics? - Supervised learning - Unsupervised learning 2. Techniques for data classification - Support Vector Machines - K-nearest neighbors - Naïve Bayes 3. Regression techniques for data prediction - Decision trees - Linear and non-linear models - Neural networks 4. Clustering techniques for grouping data and detecting patterns - K-means - Markov hidden models - Gaussian mixture models 5. New trends: Deep Learning 6. Machine learning applications in robotics - Human-Robot Interaction - Autonomous vehicles - Medicine

Magistral classes, laboratory practical sessions, individual tutorials, and personal work from the students