The graphs come handy whenever we deal with relations between the objects. This course, focused on learning, will present methods involving two main sources of graphs in ML: 1) graphs coming from networks, e.g., social, biological, technology, etc. and 2) graphs coming from flat (often vision) data, where a graph serves as a useful nonparametric basis and is an effective data representation for tasks as spectral clustering, manifold or semi-supervised learning. We discuss online decision-making on graphs, suitable for recommender systems or online advertising. Finally, we discuss the scalability of all approaches and learn how to address huge graphs in practice. The lectures show not only how but mostly why things work. Topics:
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spectral graph theory, graph Laplacians and spectral clustering
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constructing graphs from flat data - graphs as a non-parametric basis
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semi-supervised learning and manifold learning
-learnability on graphs - transductive learning
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adaptive online learning with graphs
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Graph Neural Networks.
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real-world graphs scalability and approximations
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large-graphs, approximation, sparsification and error analysis
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decision-making on graphs, graph bandits
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social networks and recommender systems applications
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vision applications (e.g., face recognition) Final grade : 16/20