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Course Description:
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Tensors and tensor decompositions are very powerful and versatile tools
that can model a wide variety of heterogeneous, multi-aspect data. As
a result,tensor decompositions, which extract useful latent information
out of multi-aspect data tensors,have witnessed increasing popularity and
adoption by the data mining community. This tutorial covers the
theory and practical algorithms for tensors for Data Mining from a
variety of perspectives. We cover topics such as CP Decomposition,
Tucker Decomposition, DEDICOM, H-Tucker, PARAFAC2, Scaling up-tensor
and its applications in real world. The tutorial covers theoretical concepts
as well as discusion of reseach papers in field of tensor factorization. Short
programming assignments include hands-on experiments with various
algorithms, and a larger course project gives students a
chance to dig into an area of their choice. This tutorial is
designed to give a all-level student a thorough grounding in
the methodologies, technologies, and algorithms
currently needed by people who do research in tensor analysis.
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Prerequisites:
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Students taking the tutorials are expected to have a pre-existing working knowledge of probability, linear algebra, statistics (basics) and algorithms (basics), though the class has been
designed to allow students with a strong numerate background to catch up and fully participate.
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Reference Book/Articals:
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- Tensor Decompositions and Applications, Tamara G. Kolda. (optional)
- Tensors for Data Mining and Data Fusion: Models, Applications, and Scalable Algorithms, Evangelos E. Papalexakis. (optional)
- Tensor Decompositions for Learning Latent Variable Models, Animashree Anandkumar. (optional)
- A First Course in Linear Algebra, Robert A. Beezer. (optional)
- Linear Algebra, David Cherney. (optional)
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Grading:
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- Homeworks (70%)
- Final project (30%)
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