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Graduate Summer School: Deep Learning, Feature LearningMonth: July 2012 Date: July 9--27 Name: Graduate Summer School: Deep Learning, Feature Learning Location: Institute for Pure and Applied Mathematics (IPAM), UCLA, Los Angeles, California. DescriptionOne of the challenges for machine learning, AI, and computational neuroscience is the problem of learning representations of the perceptual world. This summer school will review recent developments in feature learning and learning representations, with a particular emphasis on "deep learning" methods, which can learn multi-layer hierarchies of representations. TopicsWill include unsupervised learning methods such as stacked restricted Boltzmann machines, sparse coding, denoising auto-encoders, and methods for learning over-complete representations; supervised methods for deep architectures, metric learning criteria for vector-space embeddings; deep convolutional architectures and their applications to images, video, audio, and text; compositional hierarchies and latent-variable models. Encouraging the careers of women and minority mathematicians and scientists is an important component of IPAM's mission and we welcome their applications. DeadlineApplications are due March 15, 2012. Information |
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