From Supervised Learning to Contextual Bandits: The Evolution of AI Decision-Making

Author(s): Joseph Robinson, Ph.D.

Originally published on Towards AI.

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Supervised Learning: Train once, deploy static model; Contextual Bandits: Deploy once, allow the agent to adapt actions based on content and its corresponding reward. The author created a visual.

Supervised learning is a staple in machine learning for well-defined problems, but it struggles to adapt to dynamic environments: enter contextual bandits.

This blog explores the differences between supervised learning and contextual bandits. From personalization engines to real-time pricing, contextual bandits provide an edge by continuously learning from feedback.

We will work hands-on with algorithms like Thompson Sampling and LinUCB to learn when and why contextual bandits outperform traditional static models trained with supervision.

Knowing the appropriate model for a given problem is essential in our data-driven world. Supervised learning has dominated the landscape for years, providing reliable solutions for static problems.

Lately, contextual bandits have emerged as a powerful alternative as applications grow more complex and require systems that adapt in real-time. They offer a sophisticated approach to learning and decision-making, whether recommending products, setting dynamic prices, or optimizing ad placements.

If you’re navigating the challenges of user personalization, dynamic environments, or exploration-exploitation trade-offs, this blog is for you!

Note that this is the second part… Read the full blog for free on Medium.

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Published via Towards AI