Supervised vs. Unsupervised Learning

Requirements Clarification & Assessment

Objective: The primary goal is to distinguish between supervised and unsupervised learning, providing clear definitions, examples, and applications for each.

Key Points to Address:

• Define and explain supervised learning.
• Define and explain unsupervised learning.
• Provide examples of algorithms used in both learning types.
• Discuss real-world applications for each.

Understanding the Audience:

• The interviewer seeks to assess your foundational knowledge of machine learning paradigms.
• They might be interested in how well you can communicate complex concepts simply.

Potential Follow-up Questions:

• How do you decide which type of learning to use for a given problem?
• Can you provide a case study where you applied either supervised or unsupervised learning?