Introduction to Machine Learning

Speaker: Prof. Wray Buntine, Monash University

Machine Learning is now a critical component of AI systems and intelligent systems such as speech recognition.  In industry, deep learning, a new variant, is becoming synonymous with AI, and many bold claims about capabilities are given.  This tutorial will briefly cover resources to find out more about machine learning, and review the major ideas behind machine learning as well as deep learning, and their relationships.  What, for instance, was the major innovation in deep learning that changed the community so much. Machine learning is a very mathematically oriented field, so some of the basic math will be presented.  It is also a very fast moving field so recent advances will be presented broadly, such as self-supervised learning and meta-learning.


Bio:  Wray Buntine is a full professor at Monash University from 2014.  He was previously at NICTA Canberra, Helsinki Institute for Information Technology where he ran a semantic search project, NASA Ames Research Center, University of California, Berkeley, and Google.   He was also involved in a number of start-ups in Silicon Valley. He does theoretical and applied work in probabilistic methods for machine learning. He is an editor for some academic journals, is on the Steering Committee of the Asian Conference of Machine Learning, and acts as Senior Programme Committee member for some conferences.

Software Analytics in Action: A Hands-on Tutorial on Analyzing and Modelling Software Data

Speaker: Dr. Chakkrit (Kla) Tantithamthavorn


Software analytics focuses on analyzing and modeling a rich source of software data using well-established data analytics techniques in order to glean actionable insights for improving development practices, productivity, and software quality. However, if care is not taken when analyzing and modeling software data, the predictions and insights that are derived from analytical models may be inaccurate and unreliable. The goal of this hands-on tutorial is to guide participants on how to (1) analyze software data using statistical techniques like correlation analysis, hypothesis testing, effect size analysis, and multiple comparisons, (2) develop accurate, reliable, and reproducible analytical models, (3) interpret the models to uncover relationships and insights, and (4) discuss pitfalls associated with analytical techniques including hands-on examples with real software data. R will be the primary programming language. Code samples will be available in a public GitHub repository. Participants will do exercises via RStudio.

Bio: Dr. Chakkrit (Kla) Tantithamthavorn is a lecturer in the Faculty of Information Technology, Monash University, Australia. He is an emerging expert in the areas of Explainable Software Analytics and Software Quality Management, having advanced the foundations of empirical-grounded software quality theories and advanced many intelligence technologies for software quality management. His research aims to develop technologies that enable software practitioners to produce the highest quality software systems with the lowest costs. Currently, his research focused on inventing practical and explainable analytics to prevent future software defects. He is best known as a lead instructor at MSR Education 2019 about Guidelines and Pitfalls for Mining, Analyzing, Modelling, and Explaining Software Defects, and the author of the ScottKnott ESD R package (i.e., a statistical mean comparison test) with more than 8,000 downloads. More about him is available at

AWS Educate – Teach Tomorrow’s Cloud Workforce today

With the increasing demand for cloud employees, Amazon Web Services (AWS) provides an Academic Gateway for the next generation of IT professionals.  AWS Educate is Amazon’s global initiative to provide students and educators with the resources needed to accelerate cloud-related learning.

Presented by AWS, this workshop will cover the following items:

  1. AWS Educate overview & credits
  2. Career Pathways
  3. Academic Portal
  4. Student Portal overview
  5. Classrooms and sample projects

AI with Deep learning (Intermediate)


This workshop is sponsored by Data Science and AI platform at Monash Univeristy.

This intermediate-level workshop introduces how to implement and integrate the latest deep learning algorithm into your research by using the HPC cluster. The workshop is divided into four sections.

  • 9:30 – 11:00: Introduction to tensorflow & neural networks
  • 11:30 – 12:30: GAN
  • 13:30 – 15:00: Reinforcement learning
  • 15:30 – 17:00: HPC

Prerequisite: Suggested learnings : Introductory Machine Learning, Unix Shell and Command Line Interface, HPC and Python

What to bring:

Bring your own laptop. The only required software is a web browser

High Quality Paper Writing

Professor Shui Yu1

1University of Technology Sydney


High quality publication is an important metric to most of us (faculty members, especially PhD students). Many passionate young researchers lack of experience of reaching their goals. In this talk, we would like to discuss this from different perspectives as an editor, a reviewer, and an author: the key is persuasive writing with evidence. This talk will shed light for ambitious hard-working young researchers.


Shui Yu is a Professor of School of Software, University of Technology Sydney, Australia. Dr Yu’s research interest includes Security and Privacy, Networking, Big Data, and Mathematical Modelling. He has published two monographs and edited two books, more than 200 technical papers, including top journals and top conferences, such as IEEE TPDS, TC, TIFS, TMC, TKDE, TETC, ToN, and INFOCOM. Dr Yu initiated the research field of networking for big data in 2013. His h-index is 35. Dr Yu actively serves his research communities in various roles. He is currently serving a number of prestigious editorial boards, including IEEE Communications Surveys and Tutorials (Area Editor), IEEE Communications Magazine (Series Editor). He has served many international conferences as a member of organizing committee, such as publication chair for IEEE Globecom 2015, IEEE INFOCOM 2016 and 2017, and general chair for ACSW 2017. He is a Senior Member of IEEE, a member of AAAS and ACM, and a Distinguished Lecturer of IEEE Communication Society.

How to apply for ARC DECRA

Professor Bernard Mans1

1Macquarie University


This workshop will provide specific background information on the ARC DECRA funding scheme. Early career researchers who have not applied for ARC funding in the past will benefit and are strongly encouraged to attend. The workshop will provide strategic advice on how to complete a Discovery Early Career Researcher Award (DECRA) application.

The presenters, led by Professor Bernard Mans from Macquarie University, will include expert researchers who have extensive experience in the ARC funding process and on the ARC College of Experts Panels. The workshop will include a Q&A session where you can raise specific queries for your own application.

CORE Conference and Journal Rankings Workshop

Professor Lin Padgham1
1Professor in Artificial Intelligence RMIT University, Melbourne, Australia.


This workshop provides an opportunity to better understand the process used in the CORE ranking of conferences and journals, and to air and discuss suggestions for possible improvements. We will present the process and data used for the most recent conference ranking, and will discuss the outcomes of the crowd sourcing attempt at gathering data for updating the journal rankings. We will also describe tools that CORE has developed that will assist in data collection for future rounds of both conference and journal rankings.

The workshop will enable participants to have a deeper understanding of both how the rankings are managed and what some of the issues are, as well as to contribute suggestions. Participants should bring a laptop to enable looking at the ranking interface as well as the supporting documentation during the workshop.