Machine learning is devoted to design of algorithms able to learn from empirical data. This approach is especially important in advanced signal and image processing, in which sets of sensors, usually large and heterogeneous, provide a huge amount of data, usually noisy. Machine Learning is concerned with multi-dimensional and statistical signal processing, especially with problems like detection, estimation and optimization. In addition to classical supervised or unsupervised learning, reinforcement learning and semi-supervised learning, Machine Learning methods include Bayesian modeling, Markov models, Support Vector Machines and Kernel methods. Machine Learning has a wide range of topics: adaptive filtering, pattern recognition, scene analysis in computer vision, data mining, robot control, data fusion, blind and semi-blind source separation, sparse component analysis, brain-computer interfaces, hyperspectral images, cognitive radio, ML for disease people, ML for cultural heritage, etc. In addition, in many applications, hardware implementations are often very important due to the huge amount of data and real-time requirements. This workshop will be held in Grenoble (France) and it is organized by Gipsa-lab. Program and registration
