Supervised, Unsupervised, and Reinforcement Learning (Pilot)

Type of course:

Digital learning, Lesson

Language:

Duration:

10 minutes

Proficiency:

Beginner

Target:

Professionals

SUMMARY

This lesson provides an in-depth introduction to the three fundamental types of machine learning: supervised learning, where models learn from labeled data; unsupervised learning, which identifies patterns in unlabeled data; and reinforcement learning, where agents learn through trial and error to maximize rewards. It also explores real-world applications of each method in the manufacturing industry, highlighting their roles in product design, sale strategies, and fraud detection.

In this session, we’ll explore:

What supervised learning is and its applications in manufacturing
What unsupervised learning is and its applications in manufacturing
What reinforcement learning is and its applications in manufacturing
The combination of these methods in real-world scenarios

About The Author

Thi Hong Nhung Dang (Nhung) is an engineer in AI at the Grenoble Institute of Technology (Grenoble INP), France. She approaches manufacturing challenges by not only leveraging data but also considering human factors, economic perspectives, and the broader context to deliver well-rounded machine learning solutions.

Learning outcomes

By the end of this lesson, learners will be able to explain how supervised, unsupervised, and reinforcement learning work to help tackle different problems.
By the end of this lesson, learners will be able to associate common manufacturing problems with the appropriate machine learning method based on the available data, the goals, and the complexity of the problem.
By the end of this lesson, learners will be able to acknowledge the vast potential applications of these machine learning methods in manufacturing.