Course Outline

Introduction

  • Solving real-world problems through trial-and-error interactions

Understanding Adaptive Learning Systems and Artificial Intelligence (AI).

How Agents Perceive State

How to Reward an Agent

Case Study: Interacting with Website Visitors

Preparing the Environment for the Agent

Deep Dive into Reinforcement Learning Algorithms

Value-Based Methods vs Policy-Based Methods

Choosing a Reinforcement Learning Model

Using the Q-Learning Model-Free Reinforcement Learning Algorithm

Designing the Agent

Case Study: Smart Assistants

Interfacing the Agent to a Production Environment

Measuring the Results of Agent Actions

Troubleshooting

Summary and Conclusion

Requirements

  • A genral understanding of reinforcement learning
  • Experience with machine learning
  • Java programming experience

Audience

  • Data scientists
 21 Hours

Testimonials (4)

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