Tutorial: Machine Learning on Big Data (SIGMOD 2013)

Statistical Machine Learning has undergone a phase transition
from a pure academic endeavor to being one of the main drivers
of modern commerce and science. Even more so, recent results
such as those on tera-scale learning and on very large neural
networks suggest that scale is an important ingredient in
quality modeling.

This tutorial introduces current applications,
techniques and systems with the aim of cross-fertilizing research
between the database and machine learning communities.
The tutorial covers current large scale applications of Machine
Learning, their computational model and the workflow behind
building those. Based on this foundation, we present the current
state-of-the-art in systems support in the bulk of the tutorial. We
also identify critical gaps in the state-of-the-art. This leads to the closing of the seminar, where we introduce two sets of open
research questions: Better systems support for the already
established use cases of Machine Learning and support for recent
advances in Machine Learning research.

  • T. Condie, P. Mineiro, N. Polyzotis, and M. Weimer, “Machine learning on big data (sigmod tutorial),” in Sigmod conference, , 2013, pp. 939-942.
    author = {Tyson Condie and
    Paul Mineiro and
    Neoklis Polyzotis and
    Markus Weimer},
    title = {Machine Learning on Big Data (SIGMOD Tutorial)},
    booktitle = {SIGMOD Conference},
    year = 2013,
    pages = {939-942},
    ee = {http://doi.acm.org/10.1145/2463676.2465338},
    crossref = {DBLP:conf/sigmod/2013},
    bibsource = {DBLP, http://dblp.uni-trier.de}