In many scientific domains, data is coded in large tables or higher-dimensional arrays. Compressing these data into smaller, more manageable representations is often critical for extracting scientific insights. This tutorial will cover matrix and tensor factorizations - a large class of dimensionality-reduction methods that includes PCA, non-negative matrix facotrization (NMF), independent components analysis (ICA), and others. We will pay special attention to canonical polyadic (CP) tensor decomposition, which extends PCA to higher-order data arrays.

The first half of the tutorial will cover theoretical concepts and foundations of these methods, many of which are surprisingly recent results. The second half will include hands-on exercises and advice for fitting these models in practice.

Please install Python 3.x and Jupyter Notebook on your laptop to participate in the hands-on portion of the tutorial.

Please RSVP here: https://goo.gl/forms/maKhBoEd75TE9WBE3

After the tutorial, slides and resources will be posted on the computational tutorials stellar page. 
slides, references, and exercises: https://stellar.mit.edu/S/project/bcs-comp-tut/materials.html
videos: http://cbmm.mit.edu/videos?field_video_grouping_tid[0]=781