Software Automation
Training Models

Syllabus Content

Explore models of training ML, including:
- supervised learning
- unsupervised learning
- semi-supervised learning
- reinforcement learning

Supervised Learning

Supervised learning uses labeled data where both inputs and desired outputs are provided to teach the algorithm to predict outputs for new inputs. It's like having a teacher who guides the learning by showing examples with correct answers and checking the student's work.

Unsupervised Learning

Unsupervised learning discovers patterns in data without predefined labels or categories, letting the algorithm find structure on its own. It's like exploring an unknown landscape without a map, where the algorithm must independently identify natural groupings and relationships within the data.

Semi-supervised Learning

Semi-supervised learning combines a small amount of labeled data with a larger amount of unlabeled data to improve learning performance while minimizing human labeling effort. This approach reflects many real-world scenarios where obtaining labeled data is expensive or time-consuming, but unlabeled data is abundant and readily available.

Reinforcement Learning

Reinforcement learning involves an agent learning to make decisions by taking actions in an environment and receiving feedback in the form of rewards or penalties. It's like training a pet through trial and error, where the agent learns optimal behavior through ongoing interaction with its environment without being explicitly told the correct actions.

Page updated