How To Train Your AI Model

Session 2: How To Train Your AI Model

🕒 Date & Time: 4 October 2025, 11am-12pm
💻 Mode: Virtual

Artificial intelligence (AI) is no longer the future – it’s already embedded in everything we do.

From unlocking your phone with Face ID, to curating your social media feed, to helping doctors detect diseases earlier – artificial intelligence is transforming how we live, work and connect.

Facial recognition alone is projected to grow into a USD 16.5 billion industry by 20301.

Those who don’t adapt will fall behind – but it’s never too late to start.

What if you could build your own AI model – using just your face?

In this interactive session with Dr Randy Zhu, you’ll explore how AI works by building your own facial expression recognition model using Teachable Machine, a beginner-friendly tool by Google. No coding. No prior experience. Just your webcam and your most expressive self.

In this session, you’ll also:

  • Train a real AI model to detect emotions like happy, sad, or surprised
  • See your model respond to your expressions in real time
  • Discover how facial recognition is used in gaming, social media, mental health, and more

All it takes is 30 minutes – and by the end, you’ll walk away with your first working AI model and a new perspective on how machines learn from humans!

1 According to a report by Reports Insights, Facial Recognition Market (2024) https://www.reportsinsights.com/industry-forecast/global-facial-recognition-market-statistical-analysis-673841

Speaker Profile

Dr Randy ZhuDr Randy Zhu
Lecturer, Information Technology

Dr Randy received his Doctor of Philosophy (Information Technology) in Computer Science from James Cook University. He has more than 10 years of experience in managing global IT solutions, and worked closely across business functions such as Sales, Marketing, and Finance in product design.

Dr Randy’s primary research focus is in the area of human-computer interaction (HCI) for people with disabilities. Wearables, IoT and advanced computer techniques like Machine Learning have led to the upsurge of innovative applications within the healthcare sector. Dr Randy’s focus is on the adoption of community-based co-design in assistive software development: using a participatory action research approach in co-design to increase usability and adoption of assistive technology.