| Description: |
Automatic Learning of unknown functional relationships Y = f(X) between multidimensional inputs X and outputs Y, involves algorithms dedicated to the intensive analysis of large finite "training sets" of "examples" of inputs/outputs pairs (X,Y). In numerous applications such as artificial vision, shape recognition, sound identification, handwriting recognition, text classification, Automatic Learning has become a major tool to emulate many perceptive tasks. Hundreds of machine learning algorithms have been emerged in the last 10 years, as well as powerful mathematical concepts, focused on key learning features : generalisation, accuracy, speed, robustness. The mathematics involved rely on information and probability theory, non parametric statistics, complexity, functional approximation. The course will present major learning architectures : Support Vector Machines and Artificial Neural Networks, as well as Clustering techniques such as the Kohonen networks . Concrete applications will be presented.
Homework and exams : Students familiar with Mathlab or equivalent scientific softwares will have the possibility to replace a large part of the homework assignments and of the exams by applied projects involving computer simulations and implementation of algorithms taught in the course |
