Visualization for Machine Learning

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Author(s):	Xenopoulos, Peter ; Nonato, Luis Gustavo ; Silva, Claudio ; DeCarvalho, BM ; Goncalves, LMG Total Authors: 5
Document type:	Journal article
Source:	2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022); v. N/A, p. 8-pg., 2022-01-01.
Abstract
As machine learning has increased in popularity, visualization has taken an important role in analyzing and communicating aspects of machine learning models. Increasingly, visualization techniques are being used across a broad set of domains and in business-critical use cases. Oftentimes, these visualizations convey non-trivial machine learning concepts, utilize complex visual representations, or demand user interaction. In this tutorial, we seek to provide a foundational understanding, to a broad audience, of the ways in which we can use visualization for machine learning tasks. In particular, we detail visual techniques for model assessment, model understanding, and dimensionality reduction. Furthermore, we outline foundations and recent work in emerging visualization topics such as topological data analysis and understanding deep learning model internals. (AU)

FAPESP's process:	13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:	Francisco Louzada Neto
Support Opportunities:	Research Grants - Research, Innovation and Dissemination Centers - RIDC