Unsupervised learning trains a machine-learning model without labeled outputs or a specific reward. Instead, it searches the data for underlying patterns, such as groups or clusters. section 1.1
This approach avoids the often labor-intensive work of having experts label each example. It differs from supervised learning, which maps labeled inputs to known outputs, and reinforcement learning, which learns through rewards and penalties. section 1.1
Table 1.1 Types of machine learning
| Supervised learning |
Unsupervised learning |
Reinforcement learning |
|
| Description |
The model learns by mapping labeled inputs to known outputs. |
The model is trained without labels and without a specific reward. |
The model learns from its environment based on rewards and penalties. |
| Data |
Labeled data |
Unlabeled data |
No static dataset |
| Objective |
To predict the output of unseen inputs |
To discover underlying patterns in the data, such as clusters |
To determine the optimal strategy via trial and error |
Compare how supervised, unsupervised, and reinforcement learning differ in their data, objectives, and feedback. section 1.1
