ECE 6502: Tensors for Data Science

Covers the basic theory, algorithms, and applications of tensor decomposition in data science and machine learning. Matrix and tensor rank, multilinear rank, low-rank (canonical polyadic) and Tucker decomposition, identifiability, algorithms, performance bounds, sparse computations, parallelization, and applications from topic and graph mining, to mixture modeling, recommender systems, and speech / audio / language modeling and understanding.  

Total Credit Hours: 3
Spring 2019
Nikolaos Sidiropoulos
Instructor Email:

Weekly Live Sessions

Mon Tue Wed Thu Fri Sat
4:00 - 5:15 PM 4:00 - 5:15 PM

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Questions? Contact the UVA CGEP Director:

Dr. William Guilford   •   Ph: 434-243-2740