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Overview of the event

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn from data, without being explicitly programmed.

The "deep" in "deep learning" refers to the number of layers through which the data is transformed. Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to its original goals. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation.

Facts & Figures About The Event

7 Speakers
7 Topics
50 Tickets

Conference Schedule

Machine learning and Deep Learning Day (01st November, 2019)
X Topic Abstract

How big is the opportunity? Our research estimates that AI could contribute $15.7 trillion to the global economy by 2030, as a result of productivity gains and increased consumer demand driven by AI-enhanced products and services. AI solutions are diffusing across industries and impacting everything from customer service and sales to back office automation. AI’s transformative potential continues to be top of mind for business leaders: Our CEO survey finds that 85% of CEOs believe that AI will significantly change the way they do business in the next five years.

With great potential comes great risk. Are your algorithms making decisions that align with your values? Do customers trust you with their data? How is your brand affected if you can’t explain how AI systems work? It’s critical to anticipate problems and future-proof your systems so that you can fully realise AI’s potential. It’s a responsibility that falls to all of us — board members, CEOs, business unit heads, and AI specialists alike.

X Topic Abstract

Artificial Intelligence and Machine Learning gradually but steadily provide ample opportunities for discovering different technology applications for diversified financial services offerings while setting an impactful stepping stone on their impact on existing and materializing business models. Quantitative and asset management, algorithmic/ artificial intelligence-enabled financial advice, predictive analytics, credit scoring and direct lending, KYC/AML and fraud detection are among the major forces of cross-sectoral disruption within the financial services sector. In alignment to the Machine Learning in knowledge-intensive systems & Applications conference topic the 45-minute presentation will focus on 5 mainstream use cases levering real-life examples of AI-enabled business models in financial services. The presentation aims at providing the audience with an opportunity to acquire actionable and contextual insights on how to make better use of machine learning across all finance functions, how to overcome strategic and integration challenges by finding patterns and relationships and how to manage AI and Machine Learning in financial services, in line to the specific data-driven capabilities expected and foreseen by the burgeoning industry.

Topic Abstract

Dr. Dimitrios Salampasis is the Director, Master of Financial Technologies (MFinTech), Financial Technologies Innovation Research Program Leader at the Swinburne Data Science Research Institute, Senior Associate, Financial Services Institute of Australasia (FINSIA) and Lecturer of Entrepreneurship and Innovation at the Swinburne Business School, Swinburne University of Technology. Prior joining academia, Dimitrios worked in the management consulting industries being involved in global advisory and consulting activities on emerging market investing assisting companies in developing long-term strategic focus and sustainable market business strategies. Dimitrios has published in international peer-reviewed academic journals and books and his work has been presented in major international conferences and invited keynote speeches and lectures around the world. His research interests revolve around the organizational, human, technological and societal sides of innovation and open innovation in financial services and FinTech innovation. He is particularly interested in algorithmic and Artificial Intelligence (AI)-enabled financial advice, management of emerging technologies and tech-enabled business models, as well as, FinTech education and curriculum development and regulatory sandboxes for FinTech entrepreneurship. Dimitrios is an active volunteer, youth business mentor and trainer on youth entrepreneurship and has participated in numerous international programmes, workshops and summits as a presenter, facilitator and rapporteur.

X Topic Abstract

Manufacturing Industry stands to benefit the most from the application of Machine Learning for automation. Machine Learning helps create Digital Twins of the operational components, monitor, and control the production process. One of the complex manufacturing industries is Food Manufacturing, and it is under pressure to create more value from existing assets. Market capital of Food & Groceries Industry was $8.7T in 2015 and is expected to reach $12.24T by 2020, a whopping 50% increase. In this presentation, we look at high-level methodologies applied to improve the production process in general and food manufacturing in particular.

Speaker Profile

Founder & CEO of A serial Entrepreneur and a consultant having consulted Banks, Telcos and Federal Government on few AI projects. Currently working on Production Automation project for Food Manufacturing Plants.

X Topic Abstract

From the images captured from the satellite, it can be leveraged to predict future farming opportunities, best practices, and the perfect time and location for farming. Through image processing and time series analysis, hidden insights can be helpful for predicting and recommending an advanced system to help farmers.

X Topic Abstract

Analytics is an important need of a corporate world in gaining the competitive advantage in a highly competitive business environment. Customers who are mainly retirees yearning to have a considerable income after retirement select the Self-Managed Super Fund (SMSF) administrators who can provide a good return on investment for a nominal cost.
It is important monitor overall company activities for better performance and profitability of a business, the rapid growth of the size of data sets, and the increasing complexity involved in data analytics requires economical, efficient and flexible tools to enable high-performance, timely, quality and current data-intensive computing. The problem of allocating the exact number of staff, relevant type of work is required in strategically planning company growth. These pose challenges to design economical and ideal dynamic analytics infrastructure, that needs to extract data from heterogeneous systems and adapt to variation in performance in real time.

Speaker Bio:

Champ Mendis is the Chief Data Scientist of Triple A Super, an adjunct lecturer, Charles Sturt University, Hony Chair, Computer Society Chapter of IEEE VIC/TAS Section. He has more than 10 years of experience working in Artificial Intelligence & Machine Learning, Information Security and Computer Information Systems. He has worked in several industries including Finance, Defence, Education,Transport,Telecom, Construction and Insurance. He had the opportunity to work for organizations such as Colmar Brunton, University of Melbourne, ACTU, DST (formerly DSTO), University of Sydney, ACTU and ARRB. He holds PhD in Computing and Information Systems from University of Melbourne and was a member of one of the best research groups in AI & ML in Australia. In his spare time, he plays Table Tennis and Chess, cycling.

X Topic Abstract

Running AI at scale is not an afterthought! As AI matures from science to engineering, AI Solutions are being driven to the Edge and hybridized deployments that mix Edge and Cloud processing. See how various industry players are mixing AI hardware and software to control unit economics and improve customer experience across multiple use cases. You'll never train the same way again.

X Topic Abstract

HR space was unadapted to AI and machine learning for a while but the trend quickly turned. Now AI and Machine learning are the optimisers of time in human resource workflows, enabling great candidate and employee experience from hire to retirement. Machine learning models are getting very sophisticated as we gather more data; and employers are able to generate insights that predicts the employee behavior for better planning and execution. HRAPP has some of these use cases implemented and I will be covering our experience and the future possibilities with machine learning in HR Tech space

Our Sponsors

Use this opportunity to improve the visibility of your organization

Instant Sponsorship
Avail instant sponsorship at just USD 2000

Instant sponsorship includes
• Branding of your company as Bronze Sponsor – Company's Logo on the event page with cross link to your website.
• One Speaking Slot (45 min -non sales talk).
• 10% discount on registration fee for any delegate from your organization.
• Full day attendance at the event with lunch
• 1 x Roll up stand / Brochure distribution at the event
• Online Interview post of your company's senior executive at our media portal

For Silver, Gold Platinum & Titanium Sponsorship opportunites, please request for Sponsorship Brochure via email at,

Our Sponsors

Media Partners


Association partner

Digital Marketing Partner

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Promotional & Media Partners

Our Past Sponsors

Media Partner


Our Speakers

Sudeep Ghosh

Assistant Vice President


Champ Mendis

Chief Data Scientist

Triple A Super

Dr. Dimitrios Salampasis

Director, Master of Financial Technologies/Lecturer, Entrepreneurship and Innovation

Swinburne Business School, Swinburne University of Technology

KrishnaKumar “KK” Santhanam

Founder - CEO

Khitish Mohanty

Data Scientist

Department of Premier and Cabinet (Vic)

Prof. Matt Kuperholz

Partner | Chief Data Scientist

PwC Australia

Lars Olesonz



Chandra Sivasubramaniam

Co-founder and CEO

MatrixThread Pty Ltd

Want to become Speaker Please register here Register

Our Pricing

Group of three or more(Early Bird)

AUD 362

Till July 27
Group of three or more(Standard)

AUD 434

Till November 01
Individual(Early Bird)

AUD 577

Till October 01
Individual (Standard)

AUD 722

Till November 01

Our Testimonial


Who can attend Machine learning and Deep Learning Day in Melbourne?

• Data Engineers/Developers / Scientists
• Analytics Professionals
• Startup Professionals
• Scientists/Researchers
• Professors
• President/Vice president
• Chairs/Directors

And last but not the least……….
Anyone interested in Machine Learning & thrives to make the future developed and better

Why to attend Machine learning and Deep Learning Day in Melbourne?

Understand the state of development of Machine learning by exchanges, clearing houses, central counter parties and payment systems, and what it means for you.

What will you learn about?

Detecting where underlying problems and frictions exist in your organisation that will be alleviated by Machine learning technologies. Using Machine learning as a tool for innovation across your organisation

Are there any prerequisites to attend this program?


Do I need to register for the event?

Yes, all conference attendees must register in advance to attend the event.