12-03, 17:30–18:00 (UTC), Talk Track I
"Is a lion closer to being a giraffe or an elephant?"
It is not a question anyone asks.
So why address that classification problem the same as you would classification of age groups or medical condition severity?
The talk will walk you through a review of regression-based approaches for what may seem like classification problems. Unlock the true potential of your labels!
In the words of David Mumford: "The world is continuous, but the mind is discrete."
We often define categories when breaking down a real-world problem into an ML-based solution. However, real target values may be continuous or at least ordered. This is something to consider and even leverage in the design of your ML model. Are you facing what seems as a classification problem? Take a moment to understand the hidden relations between your “classes”.
Topaz will share practical tips derived from her own experience leading AI algorithmic groups and will cover an overview of approaches that may boost your classifiers!
Previous knowledge expected
Topaz Gilad is an R&D manager specializing in AI, machine learning, and computer vision, leading production-oriented innovative research.
With experience in large companies as well as startups, in various industries, from space imaging and semiconductor microscopy to sports tech, wellness, beauty, and self-care industry, she has developed methodologies to scale up while improving quality, delivery, and teamwork.
Currently VP of AI and Algorithms at Voyage81, an innovation company that excels in computer vision deep learning algorithms in both RGB and hyper-spectral domains. Previously head of AI at Pixellot, a leading AI-automated sports production company.
Topaz is also an advocate for women in tech. When she is not building algorithmic teams, she enjoys painting.