02. A Brief Overview of Unsupervised Learning Methods

Note: At 3:13, the video mentions a future segment that will appear in the next course. Please ignore this part, as it does not apply to the current course.

Understanding Unsupervised Learning

Unsupervised learning is a vital aspect of machine learning focused on finding patterns in unlabeled data. Unlike supervised learning, it doesn't rely on predetermined labels but instead explores inherent data structures.

Key Techniques:

• Clustering: Groups similar data points together.
  • K-means Clustering: Partitions data into K distinct clusters.
  • Hierarchical Clustering: Builds a tree of clusters.
  • DBSCAN: Density-based spatial clustering.
  • Gaussian Mixture Models: Uses probability distributions to identify clusters.
• Dimensionality Reduction: Simplifies data by reducing features while retaining essential information.
  • Principal Component Analysis (PCA): Transforms data to lower-dimensional spaces to understand main data variances.
  • t-SNE and SVD: Other methods for reducing dimensions.

Applications in Trading:

• Market Regime Identification: Categorizing markets as bull or bear based on historical data.
• Anomaly Detection: Spotting unusual trading patterns for opportunities or risks.
• Data Simplification with PCA: Identifying factors affecting asset prices to enhance trading model performance.

Focusing on these techniques improves understanding and handling of complex data scenarios, especially in fields with limited labeled data.