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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 (2)
interaction through exercises and also projects sharing
Claudiu - MSG system
Course - Advanced Spring Boot
How the exercises were integrated with theory and examples
