Machine Learning 101
In recent years, the field of machine learning has come to play a crucial role in decision making by commercial and government agencies and found use in a wide range of research applications, prompted by the astounding growth of data collection. This workshop will introduce the field and in doing so attempt to answer the following questions:
Who should attend: people interested in the potential use of this technology in their business.
Why attend: get up-to-date on the latest techniques and applications, receive a measured view of the technology its strengths and weaknesses.
Prof. Geoff Holmes has, in the past, been head of the machine learning group at the University of Waikato and has been involved in several open source projects over the last 20 years. Waikato’s machine learning project has had a far-reaching influence on developments in the field worldwide, principally through the open-source Weka software, one of the most widely used machine learning tools in the world today (Weka software has been downloaded 6 million times since it was first hosted at the Sourceforge website for open-source software in April 2000 – currently at a rate of about 3,000 downloads a day). Academic publications (close to 400 articles) by the machine learning group can be found at http://www.cs.waikato.ac.nz/ml/publications.html,
Professor Holmes has led the applied machine learning subgroup at the University for the past 10 years. This group has particular expertise in the deployment of machine learning solutions in practice and has developed a bespoke platform for this purpose (see https://adams.cms.waikato.ac.nz/). He has attracted major funding from FRST and MBIE (for example, Development and Application of Machine Learning Techniques 1994-1998; Fielded applications of Machine Learning 1998-2001; Formal methods and Data Mining 2002-2005; Data Mining 2007-2011; BuildIT Post-Doctoral Award for Dr. A. Bifet (2010-12); Consulting contracts with BLGG Research, Holland, ongoing since 2011; MBIE subcontract with Plant and Food 2016).
Professor Holmes has also been responsible for the development of a platform for processing very large (possibly infinite) datasets MOA (Massive Online Analysis), which is to data stream mining what Weka is to batch learning (see http://moa.cms.waikato.ac.nz/).
Aside from software, Professor Holmes has made contributions to the major conferences in machine learning and data mining (over 120 academic publications). He is currently working on a book describing the techniques contained in the MOA software. He was part of the team that in 2005 won the SIGKDD Data Mining and Knowledge Discovery Service Award for Weka and regularly serves on senior program committees for KDD, ECMLPKDD and Discovery Science and referees articles in all the major journals in the field.