AWS Certified AI Practitioner

Explanation

Unsupervised Learning (Clustering) is the correct choice because:

1. No labeled data available: The problem states "without having topic labels," which means there are no pre-defined categories or labels for the news articles.

2. Clustering purpose: Clustering algorithms are specifically designed to group similar data points together based on their features without any prior knowledge of categories.

3. Common use case: Topic modeling and document clustering are classic applications of unsupervised learning where documents are grouped based on word frequency, TF-IDF scores, or embeddings.

Why other options are incorrect:

• A) Supervised Learning: Requires labeled training data with known categories, which is not available in this scenario.
• B) Reinforcement Learning: Used for decision-making problems where an agent learns through rewards/penalties, not suitable for document grouping.
• D) Transfer Learning: Involves using knowledge from one domain to improve learning in another domain, but still typically requires some labeled data.

Common clustering algorithms that could be used for this task include:

• K-means clustering
• Hierarchical clustering
• DBSCAN
• Latent Dirichlet Allocation (LDA) for topic modeling

The key distinction is that unsupervised learning discovers patterns and structures in data without any guidance from labeled examples.