November 23-24


Phil Winder


Winder Research, UK


Dr. Phil Winder is a multidisciplinary software engineer and data scientist. As the CEO of Winder Research, a Cloud-Native data science consultancy, he helps startups and enterprises improve their data-based processes, platforms, and products. Phil specializes in implementing production-grade cloud-native machine learning and was an early champion of the MLOps movement. More recently, Phil has authored a book on Reinforcement Learning (RL) ( which provides an in-depth introduction of industrial RL to engineers.

He has thrilled thousands of engineers with his data science training courses in public, private, and on the O’Reilly online learning platform. Phil’s courses focus on using data science in industry and cover a wide range of hot yet practical topics, from cleaning data to deep reinforcement learning. He is a regular speaker and is active in the data science community.

Phil holds a Ph.D. and M.Eng. in electronic engineering from the University of Hull and lives in Yorkshire, U.K., with his brewing equipment and family.


A Code-Driven Introduction to Reinforcement Learning

Reinforcement learning (RL) is lined up to become the hottest new artificial intelligence paradigm in the next few years. Building upon machine learning, RL has the potential to automate strategic-level decisions throughout industry.

In this practical presentation, I first walk you through a little background information and a real-life cyber-security example that my company has just delivered to an international food and drink company.

Then I will present a code-driven introduction to RL, where you will explore a fundamental framework called the Markov decision process (MDP) and learn how to build an RL algorithm to solve it. This presentation includes a Jupyter notebook that you can tinker with during the presentation. Full instructions will be provided.

Although this presentation is suitable for beginners, you will benefit if you have some exposure to data science and machine learning.

Session Keywords

🔑 Reinforcement Learning
🔑 Cyber Security

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