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2021 International WEKA User Conference - Making AI accessible

Date Friday 26 November 2021
Time 9:30am - 4pm
Admission Cost Free

Waikato Environment for Knowledge Analysis (Weka), developed at the University of Waikato, New Zealand, is an open source software combining an easy to navigate and use graphical interface with powerful visualisation tools and algorithms for amongst other things data classification, mining, and analysis as well as predictive modelling. Downloaded more than 12 million times since its release in the 1990’s WEKA is one of the most popular platforms for learning machine learning.

2021 International WEKA User Conference

Making AI accessible

Waikato Environment for Knowledge Analysis (Weka), developed at the University of Waikato, New Zealand, is an open source software combining an easy to navigate and use graphical interface with powerful visualisation tools and algorithms for amongst other things data classification, mining, and analysis as well as predictive modelling. Downloaded more than 12 million times since its release in the 1990’s WEKA is one of the most popular platforms for learning machine learning.

Event Details

When:
9.30am to 16.00pm, Friday November 26, 2021 (NZ Time - GMT+13)

Location:
Online

WEKA is designed to support domain experts and as such all around the world the platform has enabled the application of machine learning across a number of sectors and in addressing numerous real-world problems.

This event will bring together a number of speakers to talk about the history of WEKA, developments since its original release, what is being planned for future versions, and some of the research and industry applications WEKA has supported.

Programme

TimeSession

09.30

Welcome

Albert Bifet
Director - Artificial Intelligence Institute, University of Waikato

 

Open Source Software at University of Waikato

Albert Bifet
Director - Artificial Intelligence Institute, University of Waikato

10.00

OpenML Benchmarking Suites

Jan van Rijn
Universiteit Leiden

10.35

Q&A

10.40

Coffee and stretch break

11.00

Automated Machine Learning in WEKA with ML-Plan

Felix Mohr
Universidad de La Sabana - Colombia.

11.35

Q&A

11.40

Weka/Python Interoperability: Using Python from Weka

Mark Hall
University of Waikato

12.15

Q&A

12.20

Taking Weka to the next level with ADAMS 

Peter Reutemann
University of Waikato

12.55

Q&A

13.00

Lunch and stretch break

13.30

Introduction to MOA

Heitor Gomes
University of Waikato

14.05

Q&A

14.10

MEKA: A Multi-label Extension to WEKA

Jesse Read
Ecole Polytechnique, Paris, France

14.45

Q&A

14.50

Coffee and stretch break

15.10

Bridging the gap: Scripting Weka from Python

Peter Reutemann
University of Waikato

15.45

Q&A

15.50

Closing comments

Albert Bifet
Director - Artificial Intelligence Institute, University of Waikato

Event Coordinator - Jannat Maqbool

Keynote


Albert Bifet

Albert Bifet

Artificial Intelligence Institute, New Zealand

Albert previously worked at Huawei Noah's Ark Lab in Hong Kong, Yahoo Labs in Barcelona, and UPC BarcelonaTech. He is the co-author of a book on Machine Learning from Data Streams published at MIT Press. He is one of the leaders of MOA, scikit-multiflow and Apache SAMOA for implementing algorithms and running experiments for online learning from evolving data streams. He was serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of KDD BigMine (2019-2012), and ACM SAC Data Streams Track (2021-2012).

Speakers


Peter Reutemann

Peter Reutemann

University of Waikato, New Zealand

After obtaining his masters degree in Freiburg/Germany, Peter started his career in open-source development and data science at the University of Waikato. A long time contributor to Weka since 2004, he spent 2005-2007 as main Weka maintainer, before moving on to other projects with a commercial focus such as ADAMS and hobby projects like python-weka-wrapper3.

Other open-source projects that he has been involved in over the years are MOA and MEKA. Recently, the focus has shifted somewhat towards deep learning using Python and containerizing these frameworks with Docker to speed up modeling/deployment. Outside university, Peter organizes the Linux and Python meetups in Hamilton.

Mark Hall

Mark Hall

University of Waikato, New Zealand

Mark Hall is one of the original core developers of the Weka machine learning software and is currently a developer and data scientist at Pyramid Analytics. He has 15 years experience as an academic researcher in computer science and has published in machine learning conferences and journals.

Prior to joining Pyramid, Mark lead Pentaho's machine learning solutions and, after Pentaho's acquisition by Hitachi, worked as chief machine learning architect with Hitachi Vantara Labs. Mark held teaching and postdoctoral positions at the University of Waikato, and is currently an honorary research associate with the computer science department's machine learning group.

Heitor Gomes

Heitor Gomes

Artificial Intelligence Institute, New Zealand

Heitor Murilo Gomes is a Senior Research Fellow and the Head of the MOA Lab at the University of Waikato. His main research interest revolves around machine learning applied to streaming data, especially delayed and partially labelled data, ensemble learning, distributed learning and unsupervised drift detection.

He contributes to several open data stream mining projects, such as the Massive Online Analysis (MOA) framework. He has served as a programme committee member of several conferences. In particular, he has been the Virtual Chair of the IEEE ICDM 2021 and the Co-Chair of the ACM SAC Data Streams Track 2021.

Jannat Maqbool

Jannat Maqbool

Artificial Intelligence Institute, New Zealand

Jannat is a CPA and former CIO with a Masters in Digital Business from the University of Waikato and a focus on leveraging technology in innovative ways to benefit people and planet. Jannat's initial career was in the financial services industry leading mid to large-scale technology adoption initiatives, following which she spent more than a decade in the vocational education sector. More recently Jannat has been involved with initiatives supporting digital inclusion and enablement.

Jannat is the Associate Director at the University of Waikato’s Artificial Intelligence Institute.

Jan N. van Rijn

Jan N. van Rijn

Leiden University, The Netherlands

Jan obtained his PhD in Computer Science in 2016 at Leiden Institute of Advanced Computer Science (LIACS), Leiden University (the Netherlands). During his PhD, he made several funded research visits to the University of Waikato (New Zealand) and University of Porto (Portugal). After obtaining his PhD, he worked as a postdoctoral researcher in the Machine Learning lab at University of Freiburg (Germany), headed by Prof. Dr. Frank Hutter, after which he moved to work as a postdoctoral researcher at Columbia University in the City of New York (USA).

He currently holds a position as assistant professor in the ADA group at LIACS, Leiden University (ada.liacs.nl). His research aim is to democratize the access to machine learning and artificial intelligence across societal institutions. He is one of the founders of OpenML.org, an open science platform for machine learning. His research interests include artificial intelligence, automated machine learning and metalearning.

Felix Mohr

Felix Mohr

Universidad de La Sabana, Colombia

Felix Mohr is a professor in the Faculty of Engineering at Universidad de la Sabana in Colombia. His research focus lies in the areas of Automated Machine Learning and Stochastic Tree Search.

He received his PhD in 2016 in the field of Automated Service Composition from Paderborn University in Germany.

Jesse Read

Jesse Read

Ecole Polytechnique, France

Jesse Read is a Professor in the Computer Science Laboratory of Ecole Polytechnique in France since 2019, after joining as Assistant Professor in 2016. He obtained his PhD from the University of Waikato in 2010, followed by postdoctoral research in the Carlos III University of Madrid (Spain), Aalto University (Finland), and Télécom ParisTech (France).

His research focus is mainly in machine learning, and particularly multi-label learning and models for data streams. He has also picked up interests in Monte Carlo methods and reinforcement learning, and has been involved in numerous applied data-science projects in medicine, biology, transport, wireless sensor networks, and other domains. Jesse obtained his BCMS (Hons) degree from the University of Waikato in 2005.

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