Seminar and Conference News

AusDM 2018 submission deadline extended to 3 August

posted Jul 16, 2018, 10:35 PM by Yanchang Zhao   [ updated Jul 16, 2018, 10:35 PM ]

The 16th Australasian Data Mining Conference (AusDM 2018)
Bathurst, Australia,
28-30 November 2018 

The Australasian Data Mining Conference has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. It is devoted to the art and science of intelligent analysis of (usually big) data sets for meaningful (and previously unknown) insights. This conference will enable the sharing and learning of research and progress in the local context and new breakthroughs in data mining algorithms and their applications across all industries.

Since AusDM02 the conference has showcased research in data mining, providing a forum for presenting and discussing the latest research and developments. Built on this tradition, AusDM18 will facilitate the cross-disciplinary exchange of ideas, experience and potential research directions. 

This year AusDM is proud to announce that conference proceedings will be published in Springers Communication in Computer and Information Science.

Publication and topics

We are calling for papers, both research and applications, and from both academia and industry, for presentation at the conference. Accepted papers will be published in the AusDM 2018 proceedings by Springer. Some selected papers will be invited for submission with extension in a special edition of a Springer journal. More details are available at our website.

Keynote speakers

Professor Junbin Gao, The University of Sydney
Professor Geoff Webb, Monash University

Important Dates

Paper Submission: extended to 3 August 2018
Authors Notified: Monday 1 October 2018
Camera Ready Submission: Monday 15 October 2018
Preliminary Program Available: Wednesday 31 October 2018
Early Bird Cut-Off Date for Authors: Friday 2 November 2018
Conference Dates: Wednesday 28 - Friday 30 November 2018

Join us on LinkedIn:

Seminar: Real-Time Analytics and Petabyte-Scale Open Data Science, Canberra, Tuesday 26 June 2018

posted May 28, 2018, 4:47 PM by Yanchang Zhao   [ updated May 28, 2018, 4:47 PM ]

This is a joint event with the Statistical Society of Australia (SSA) Canberra Branch. There will be two talks starting at 5:45pm at the event. Each talk will be of 30m presentation plus 15m Q&A. There will also be pre-talk drinks and nibbles from 5pm to 5:45pm, kindly sponsored by M&T Resources.


Date: Tuesday 26 June 2018
Venue: ANU campus
5:00-5:45pm Pre-talk drinks and nibbles, Allan Barton Forum, 2nd floor CBE Building, ANU
5:45-6:30pm Talk #1 - Solution for real-time analytics embedded in business process, by Richard Gao, Australian Government
6:30-7:15pm Talk #2 - Machine Learning and analytics using an open data science approach, by Rishu Saxena, Cloudera

Talk #1:

Topic: Solution for real-time analytics embedded in business process - Orchestra of elements to deliver real-time analytics as a critical part of business process
Speaker: Richard Gao, Australian Government
Abstract: The talk will presents three evolution stages of decision-making process: 1) time consuming non-automatic decision making process, including data collection, analysis, report and decision making, 2) automatic decision making process as part of general business process (e.g. Operational Decision Manager), and 3) modern data science-driven business process, including machine learning algorithm development and real-time algorithm deployment systems as new feature.
Bio: Mr. Richard Gao, is a lead data scientist in the Commonwealth Australia, previously worked as a data analyst and statistician across multiple Commonwealth Departments for over 10 years. Before that, he worked as an R&D manager with Huawei Technologies and Math teacher with a local Chinese university. He holds Master of Applied Statistics, MBA and Bachelor of Applied Mathematics.

Talk #2:

Topic: Machine Learning and analytics using an open data science approach
Speaker: Rishu Saxena, Cloudera
Abstract: Data Science in an enterprise environment with strong security restrictions, mean that leveraging the latest publicly available algorithms, libraries and approaches is difficult. In this talk we will propose an approach to leverage petabyte-scale open data science, while still maintaining security and resource controls.
Bio: Rishu is System Engineer with Cloudera focusing on helping customers to leverage machine learning. Rishu has previously worked with public, research and private sector organisations to help define the vision, strategy, technical solutions and delivery approach for big data hubs, advanced analytics and visualisation. Rishu is currently studying a Masters of Data Science at Sydney University.

Big Data Innovation Summit & Machine Learning Innovation Summit, San Francisco

posted Mar 15, 2018, 3:21 PM by Yanchang Zhao   [ updated Mar 15, 2018, 3:22 PM ]

Understand how you can articulate your current skills/responsibilities and take the career path that you want. Read this article from our expert Hugh Williams, ML Data Science Manager at Uber:


Would you like to learn more about data, NLP, AI, Deep Learning?


Join two San Francisco events:


* Big Data Innovation Summit, April 12 & 13 

> Visualize, explore data and generate insights to make better decisions

> Maximize your delivery of valuable insights with an effective Data Intelligence program

> Leverage AWS components to build and test multiple attribution models at scale and find AWS patterns



* Machine Learning Innovation Summit, May 9 & 10

> Master how advanced analytics can help you make better decisions on promotions

> Discover how ML can highlight the most relevant products and increase customer satisfaction

> Learn how ML can provide you with real-time recommendations using resources efficiently and operating on a massive scale



Enjoy an exclusive $200 off all two-day passes with the code RDM200 when you register! For more information on the events and group rates, please contact Lewis:  


PS: Learn more about NLP and how Uber is using it. Know how to adopt ML by reading this 

interview with Hugh Williams, ML Data Science Manager at Uber:

New E-learning Course by Prof. Dr. Bart Baesens: Profit-Driven Business Analytics!

posted Jan 10, 2018, 2:53 AM by Yanchang Zhao   [ updated Jan 10, 2018, 2:56 AM ]

The e-learning course on profit-driven business analytics presents a toolbox of advanced analytical approaches that support cost-optimal decision making. They are advanced in that they are tailored for use in a business setting, where it is crucial to account for the costs and benefits that are related to decision making based on the output of analytical models. We call such approaches profit-driven analytics and they extend and reinforce the abilities of traditional analytics. The profit-driven perspective towards analytics that is advanced in this course contrasts with a traditional statistical perspective, which ignores the costs and benefits related to decision making based on analytical models.

In the course, we discuss both profit-driven descriptive and predictive analytics, and as well introduce uplift modeling as a stepping stone toward developing prescriptive analytical models. We also discuss a range of profit-driven evaluation measures for assessing the performance of analytical models from a business perspective. Finally, we conclude by looking into the economic impact of adopting analytics and zoom into some practical concerns related to the development, implementation and operation of analytics within an organization.

The E-learning course consists of more than 7 hours of movies, each 5 minutes on average.  Quizzes are included to facilitate the understanding of the material. Upon registration, you will get an access code which gives you unlimited access to all course material (movies, quizzes, scripts, ...) during 1 year. The course focusses on the concepts and modeling methodologies and not on the SAS software.  To access the course material, you only need a laptop, iPad, iPhone with a web browser. No SAS software is needed.  See this link for more details.  

Course URL:

Bart Baesens

Professor Bart Baesens is a professor of Big Data & Analytics at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom).  He wrote more than 200 scientific papers on Big Data & Analytics, Machine Learning, Credit Risk Modeling and Fraud Detection and has won various awards for his research (e.g. Goodeve medal for best JORS paper, best EJOR 2013 and 2016 paper).  His findings have been published in well-known international journals and presented at international top conferences.  He is the author of 6 books Analytics in a Big Data World, Fraud Analytics using Descriptive, Predictive and Social Network Techniques, Credit Risk Management: Basic Concepts, Credit Risk Analytics and Profit Driven Business Analytics.  He also teaches E-learning courses on Advanced Analytics in a Big Data World, Fraud Analytics, Social Network Analytics and Credit Risk Modeling.  His research is summarized at  He regularly tutors, advises and provides consulting support to international firms with respect to their  big data, analytics and credit risk management strategy. 

Wouter Verbeke

Wouter Verbeke, Ph.D., is an assistant professor at Vrije Universiteit Brussel (Belgium). His research is mainly situated in the field of predictive, prescriptive and network analytics, and is driven by real-life business problems including applications in customer relationship, credit risk, fraud, supply chain and human resources management. In 2014, he won the EURO award for best article published in the European Journal of Operational Research in the category Innovative Applications of O.R. His research is summarized at

CFP: AusDM 2018, Bathurst, Australia, 28-30 Nov 2018

posted Nov 26, 2017, 2:45 PM by Yanchang Zhao   [ updated Nov 26, 2017, 2:48 PM ]

16th Australasian Data Mining Conference (AusDM 2018)
Bathurst, Australia,
28-30 November 2018 

The Australasian Data Mining Conference has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. It is devoted to the art and science of intelligent analysis of (usually big) data sets for meaningful (and previously unknown) insights. This conference will enable the sharing and learning of research and progress in the local context and new breakthroughs in data mining algorithms and their applications across all industries.

Since AusDM'02 the conference has showcased research in data mining, providing a forum for presenting and discussing the latest research and developments. Built on this tradition, AusDM'18 will facilitate the cross-disciplinary exchange of ideas, experience and potential research directions. Specifically, the conference seeks to showcase: Research Prototypes; Industry Case Studies; Practical Analytics Technology; and Research Student Projects. AusDM'18 will be a meeting place for pushing forward the frontiers of data mining in academia and industry.

Publication and topics

We are calling for papers, both research and applications, and from both academia and industry, for presentation at the conference. All papers will go through double-blind, peer-review by a panel of international experts. Accepted papers will be published in the AusDM 2018 proceedings by Springer. Some selected papers will be invited for submission with extension in a special edition of a Springer journal. Please note that we require that at least one author for each accepted paper will register for the conference and present their work. One full registration will cover at most two papers.

AusDM invites contributions addressing current research in data mining and knowledge discovery as well as experiences, novel applications and future challenges. Topics of interest include, but are not restricted to:

- Applications of Data Mining and Case Studies - Big Data Analytics
- Biomedical and Health Data Mining
- Business Analytics
- Computational Aspects of Data Mining
- Data Integration, Matching and Linkage
- Data Mining Education
- Data Mining in Security and Surveillance
- Data Preparation, Cleaning and Preprocessing 
- Data Stream Mining
- Implementations of Data Mining in Industry
- Integrating Domain Knowledge
- Knowledge Discovery and Presentation
- Link, Tree, Graph, Network and Process Mining 
- Multimedia Data Mining
- Mobile Data Mining
- New Data Mining Algorithms
- Privacy-preserving Data Mining
- Spatial and Temporal Data Mining 
- Text Mining
- Web and Social Network Mining

Keynote speakers

As is tradition for AusDM we have lined up an excellent keynote speaker program. Each speaker is a well known researcher and/or practitioner in data mining and related disciplines. The keynote program provides an opportunity to hear from some of the world's leaders on what the technology offers and where it is heading.

Submission of papers

We invite three types of submissions for AusDM 2018:

- Academic submissions: Regular academic submissions can be made in Research Track reporting on research progress, with a paper length up to 12 pages. For academic submissions we will use a double-blind review process, i.e. paper submissions must NOT include author names or affiliations (and also not acknowledgements referring to funding bodies). Self-citing references should also be removed from the submitted papers (they can be added on after the review) for the double blind reviewing purpose.

- Industry submissions: Submissions can be made in the Application Track to report on specific data mining implementations and experiences in governments and industry projects. Submissions in this category can be up to 12 pages. These submissions do not need to be double-blinded. A special committee made of industry representatives will assess industry submissions.

- Industry Showcase submissions: Submission from industry and government on an analytics solution that has raised profits, reduced costs and/or achieved other important policy and/or business outcomes can be made in this track with a one page Abstract only.
Paper submissions are required to follow the general format specified for papers. LaTeX styles and Word templates will be available while LaTeX will be the recommended typesetting package.

The electronic submissions must be in PDF only, and made through the AusDM'18 Submission Page.

Important Dates

Paper Submission Closed: Friday 20 July 2018
Authors Notified: Monday 1 October 2018
Camera Ready Submission: Monday 15 October 2018
Preliminary Program Available: Wednesday 31 October 2018
Early Bird Cut-Off Date for Authors: Friday 2 November 2018
Conference Dates: Wednesday 28 - Friday 30 November 2018

Seminar and Panel Discussion: Career of Data Scientist and Analyst, Canberra, 7 Sept 2017

posted Aug 29, 2017, 8:51 PM by Yanchang Zhao   [ updated Aug 29, 2017, 8:55 PM ]

Topic: Career of Data Scientist and Analyst  
    Dharmendra Sharma, Professor, University of Canberra   
    Zunaeed Kamal, Regional Director, M&T Resources  
    Tilly Tan, Manager, PWC  
Date and Time: 4:30-6:30pm Thursday 7 Sept 2017. Food and drinks will start at 4:30pm and talk will start at 4:50pm.   
Location: 12B02 (Building 12, room B02), University of Canberra  
Map and Parking:   

Acknowledgement: Thanks to UC for providing a venue, and M&T Resources for sponsoring food and drinks. 


This is a seminar and panel discussion on the career of Data Scientist / Analyst. It consists of 

  • 30 minutes talk, composed of a 10 minutes talk by each of three panelists, followed by
  • 50 minutes panel discussions, with three panelists answering questions from audience. 

Three panelists will present and provide their valuable experiences in topics below.

  • Dharmendra Sharma (Professor, UC): what is data science, state of the art and future of data science, data science and business analytics programs at uni, and options for students at UC 
  • Zunaeed Kamal (Regional Director, M&T Resources): job market of data scientist / analyst and its trend, required skillsets for various roles, permanent vs contractor roles, salary and rates, 
  • Tilly Tan (Manager, PWC): experiences of and difference between data scientist and analyst in public sector, private sector and uni; experience, lessons and career development in consulting in the area of data science  


Dharmendra Sharma

Professor Dharmendra Sharma is currently the Chair of University Academic Board and Professor of Computer Science at the University of Canberra (UC).  Prof Sharma's research background is in the Artificial Intelligence areas of Planning, Data Analytics and Knowledge Discovery, Predictive Modeling, Constraint Processing, Fuzzy Reasoning, Brain-Computer Interaction, Hybrid Systems and their applications to health, education, security, digital forensics and sports. He has published over 270 research papers and has supervised to completion over 30 higher degrees research students. He is a Fellow of the Australian Computer Society, a Fellow of the South Pacific Computer Society, and a Senior Member of IEEE.  

Zunaeed Kamal 

Mr. Zunaeed Kamal is ACT Regional Director at M&T Resources. Established in 1994, M&T Resources helps leading organisations achieve technology and business success through innovative talent engagement, attraction and retention. With a purpose to “help people achieve greater success”, their relentless focus on candidate aspirations and client outcomes has led them to become one of Australia’s most awarded recruitment firms. Zunaeed manages the Canberra team, providing exceptional resourcing services to government and private sector clients. He pays close attention to the latest business and IT labour trends affecting the Canberra market, with a particular focus on the federal government’s strategies and activities. He prides himself on providing a highly consultative service to his clients. He seeks to deeply understand the outcomes clients are seeking for their projects, allowing M&T Resources to act as a true partner in the ongoing transformation of their business. 

Tilly Tan 

Ms. Tilly Tan is a Manager in the Canberra office of PWC’s EIM team. She has over 9 years of experience as a data analyst and data scientist working in multiple industry sectors. She has the combination of strong technical and leadership skills that has helped deliver complex data analytics programs, with the ability to analyse data in regard to business applications.

Call for Participation: Big Data Summit 2017, Melbourne, 20-22 August

posted Jul 9, 2017, 8:31 PM by Yanchang Zhao   [ updated Jul 9, 2017, 8:31 PM ]

Call for Participation

Big Data Summit 2017

Co-located with IJCAI 2017

20-22 August 2017, Melbourne





This three-day event provides a unique, independent, cross-domain and non-commercial view for the advances, future, best practice and policies for artificial intelligence and data science-driven research, innovation, economy and society.


Link for and More Information about Online Registration


20 Aug: AI/Data science Discipline Course

Discipline lecture series about the advances and future directions of major AI/data science areas, to be delivered by world leaders:

-       Mathematical modeling & bitcoin blockchain

Prof Peter Taylor, ARC Laureate Fello

University of Melbourne, Australia

-       Computer vision meets machine learning

Prof Dacheng Tao, ARC Laureate Fellow, IEEE Fellow

University of Sydney, Australia

-       Knowledge discovery & social analytics

      Prof Joao Gama, ACM Distinguish Speaker

University of Porto Porto, Portugal

-       Advances in machine learning

      Prof Qiang Yang, IEEE/AAAI Fellow, Head of Department

Hong Kong University of Sci & Tech

-       Advances in big graph/data processing

      Prof Xuemin Lin, IEEE Fellow

Univ. New South Wales, Australia

-       Advances in Internet of Things/Cloud computing

      Prof Michael Sheng, Head of Department, ARC Future Fellow

Macquarie University, Australia

-       Ethics in artificial intelligence

      Prof Toby Walsh, Fellow of Australian Academy of Science, Data61

UNSW, Australia


21 Aug: AI/Data science Big Data Forum

Keynote and invited speeches by industry, government and research leaders, plus two exciting panels:

-       Keynote speech

            Dr Usama Fayyad, ACM/IEEE Fellow, CEO, Open Insights

-       Invited distinguished speakers

            Prof James Bailey, Melbourne University

            Dr Paul Beinat, Director, Neuronworks

            Dr Fang Chen, Group Leader, Data61 | CSIRO

            Dr Frederic R Clarke, Director, Australian Bureau of Statistics

            Dr Warwick Graco, ATO

            Dr Kyusik Kim, Director, IP Australia

            Dr Dickson Lukose, Chief Data Scientist, GCS Agile

            Dr Paul Rybicki, Head of TV and Content, Optus

            Dr Amy Shi-nash, Head of Data Science, Commonwealth Bank of Australia

            Prof Yun Yang, Swinburne University of Technology

-       Panel: Data Science: Scientific Revolution?

-       Panel: Data/Intelligence: New Economy Engine?

            Moderator: Dr Warwick Graco, ATO


 22 Aug: AI/Data science Executive Workshop

Global best practice and real-life enterprise analytics case studies for data and business executives and managers to make informed and smarter decisions:

            Dr Usama Fayyad, CEO, Open Insights

            Prof Geoff Webb, Monash University

            Prof Longbing Cao, University of Tech Sydney

            Dr Paul Beinat, Director, Neuronworks


Seminar: End-to-end development and deployment of data science models with IBM Watson Data Platform, Canberra, Thursday 27 July 2017

posted Jul 2, 2017, 4:44 PM by Yanchang Zhao   [ updated Jul 2, 2017, 4:46 PM ]

Topic: End-to-end development and deployment of data science models with IBM Watson Data Platform 
Speakers: Ross Farrelly and Stuart Maclean, IBM 
Date and Time: 4:00-6:00pm Thursday 27 July 2017. Food and drinks will start at 4:00pm and talk will start at 4:30pm.  
Location: Canberra 
Acknowledgement: Thanks to IBM for sponsoring venue, food and drinks.

Organisations need streamlined solutions to not only develop but also to deploy data science models. This presentation will walk through an end to end model development and deployment workflow using the cloud based Watson Data Platform suite of solutions. The Watson Data Platform features flexible, composable services, open source foundation and full support for today’s diverse data types, and its solution components include ingestion and transformation services, storage solutions, analytical engines, a deployment platform and governance tools for data lineage and quality. 


Ross Farrelly 
Ross Farrelly is the Cloud Strategy Leader for the Australia and New Zealand region. He works with companies throughout the region to develop and execute on their strategies to adopt and realize the benefit of cloud computing, focusing on the IBM Cloud Data Services technologies. He has a Master of Applied Statistics, a Masters of Applied Ethics, a first class honours degree in Pure Mathematics and is aiming to compete his PhD in Information Systems this year. 

Stuart Maclean 
Stuart has over twenty year’s experience architecting, managing and implementing technology solutions through the full development lifecycle across a broad spectrum of industry including telecommunications, utilities, insurance, finance and medical. His specific areas of expertise are in architecture consulting and solution delivery with an emphasis on assisting customers to develop and deliver business led technology solutions comprising custom application development, packaged application customization and application integration solutions.

Presentation Slides:

Slides of our previous seminars, including last week’s presentation on Apache NiFi, are now available on Google Drive at link below.

The 2017 Big Data Summit, Melbourne, 20-22 August 2017

posted Jun 14, 2017, 4:10 AM by Yanchang Zhao   [ updated Jun 14, 2017, 4:11 AM ]

Invitation to


2017 Big Data Summit

20-22 August 2017, Collocated with IJCAI2017,

in Melbourne Convention & Exhibition Centre


Early-bird online registrations are now open for significant discount.

We take this privilege opportunity to invite you to participate in the 2017 Big Data Summit (BDS’2017):

  • The 2017 Summit Theme: Artificial Intelligence & Data Science (AI/DS)
  • 20th - 22th Aug 2017: Three day event: 20/8: AI/DS Discipline Courses, 21/8: AI & Data Science Forum, and 22/8: AI/DS Executive Workshop, Melbourne Convention and Exhibition Centre
  • Co-located with IJCAI2017, the largest AI conference, first-time in Australia
  • World-class keynote and invited speakers from global academic, industry and government communities
  • The summit was established in 2012, a forum dedicated to big data, data science, analytics, AI 2.0, data economy and innovation
  • An independent, cross-disciplinary, and cross-domain platform for promoting the gap-bridging between data science research and practices
  • This annual event attracted participants largely from industry and government, BDS’2015 co-located with KDD2015 attracted 450 registrations.
  • BDS15 is proudly supported/sponsored by ACM SIGKDD Australian & NZ Chapter and other institutions and professional bodies

The theme of this year’s Big Data Summit is “Artificial Intelligence & Data Science”. 

Since the Summit’s inception in 2012, we have seen increasing interest and investment within both industry and government in data-led innovation and industralisation to deeply explore big data universe, invent data science, train data scientists and engineers, and develop the data economy.

This year’s event aims to provide data professionals, decision-makers and policy-makers with a global and regional perspective to outline the data science and AI advances, best practices, future directions, data economy and industrialization. The Summit will showcase global advancements and best practices, explore thought-provoking insights, and demonstrate global solutions and lessons learned across industry, government and academia by global AI and data science leaders.

AI/DS Discipline Courses, 20 Aug 2017

  • Seven carefully selected interdisciplinary discipline lectures on AI/DS latest advancements, and future scientific directions
  • Lecturers are all world-leading active professors in respective areas
  • Major topics: Data science, big data, mathematical modelling, artificial intelligence, machine learning, deep learning, data mining, computer vision, Internet/Web of Things, and AI ethics

Big Data Forum, 21 Aug 2017

  • Latest progress and experiences about AI/DS-driven innovation, transformation and best practices
  • Mixed global and regional perspectives, best practices, and experiences
  • All speakers are senior data professionals and leaders in major industry and government organizations
  • Addressing the latest AI/DS technologies, solutions, services, applications, and policies
  • Covering major areas and domains including banking, finance, insurance, telecommunications, government services, and corporate analytics
  • Panel on Data Science: Scientific Revolution?
  • Panel on Data/Intelligence: Future Economic Engine?

AI/DS Executive Workshop, 22 Aug 2017

  • Designed for enterprise executives who lead AI/DS strategies and portfolio
  • The big value, trends, and potential of data to economy and smart decision
  • The global perspectives, experiences, and best practices of data and intelligence economy
  • Informed decisions on enterprise-wide data decisions, solutions, practices and competency
  • Real problem-based deep analysis of enterprise transformation gaps, strategies and opportunities
  • Interactive and personalized mentoring, teamwork, case studies, arguments, and collaborations
  • World top industry and research leaders in AI and DS as mentors and collaborators
  • Collaborative work with senior peers from different domains and backgrounds

What Will be Highlighted in the 2017 Summit?

  • Global advances in scientific development in data and analytics science and AI 2.0
  • Global best practices in data economy and industrial transformation
  • Global perspective of AI & data science trends and directions by leading data/AI scientists from international communities
  • The progress and future of AI & data science in Australia and New Zealand
  • Meeting global research and business leaders in data science & AI
  • Thought and operations leadership and experience in leading data science 
  • Competency, practices, policies and processes in managing data science projects
  • High utility and impact data science case studies and showcases


Who Should Attend?

  • Data executives, such as Chief Data Scientists and Chief Data/Analytics Officer
  • Data scientists and data engineers
  • Data modellers, business analysts and business intelligence operators
  • Data & analytics professionals
  • Business decision makers
  • Policy executives
  • Government data analysts and policy-makers
  • Academics (including research students and early career researchers)

Why Should You Attend?

Started in 2012, the annual Summit attracted hundreds of participants from industry, government and academia. The Summit is the premier and unique forum for bridging the gaps between academia, industry and government, and sharing independent insights on the advancement, best practices, trends and controversies about data science, big data, artificial intelligence, and data economy.

With very prestigious speakers across academic, industry and government from the global communities, the 2017 Summit will cover a broad spectrum of data science and AI aspects and domains. The three-day program will substantially lift your

·       vision, knowledge and capabilities in advancement through the AI/DS Discipline Courses,

·       innovation and best practices by the AI/DS Forum, and

·       leadership and strategic decision-making via the AI/DS Executive Workshop.

This unique program to establish a big picture and hands-on grasp of AI & Data Science thought leadership, innovation, services, education and economy.

Co-located with IJCAI2017, BDS’2017 attracts global interest, will mark a unique and high quality opportunity for you and your organization to grasp the cutting-edge and thought-leading progress, network with peers and thought leaders, and most importantly, dig out more insights and value from your big data and lift your personal and organization competency in AI & Data Science in the increasingly competitive and challenging profession, economy and environment.

For More Information

For more details about the Summit, please visit the Website (

For registration to BDS’2017, please Check and Register Here.

For any other inquiries about the 2017 Summit, please feel free to
Contact us.

Tutorials on Machine Learning with Rattle and R, Melbourne, 1 June 2017

posted May 21, 2017, 4:46 PM by Yanchang Zhao   [ updated Jun 5, 2017, 10:55 PM ]

Dr. Graham Williams and myself will run tutorials on Machine Learning with Rattle and R – Decision Trees, Ensemble Models, Association Rules, Text Mining and Social Network Analysis, at the Melbourne Data Science Week, 1 June 2017. Only 3 spots left.

See tutorial details at



Download tutorial materials (incl. PDF slides and R scripts) at

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