01. Introduction to Unsupervised Learning

Exploring Unsupervised Learning in AI

Unsupervised learning focuses on uncovering patterns and structures within unlabeled data. Unlike supervised learning, where learning occurs from labeled examples, unsupervised techniques reveal hidden structures without specified labels. This can be especially useful in cases such as financial data analysis.

Key Techniques

• Clustering: Involves grouping similar data points. A common approach is K-means clustering.
• Dimensionality Reduction: Simplifies datasets while preserving essential information. Principal Component Analysis (PCA) is a widely-used method for this.

Applications in the Stock Market

• Identifying Similar Stocks: Group stocks with similar performance or characteristics.
• Recognizing Market Regimes: Detect different states or cycles in the market.
• Simplifying Data: Reduce data complexity to detect broader trends.

Learning Objectives

• Understand fundamental unsupervised learning concepts.
• Apply K-means and PCA to stock market data.
• Extract insights aiding investment decisions and risk management through practical exercises.