Unsupervised Learning Dimensionality 

This course is designed to provide a comprehensive understanding of unsupervised learning techniques, with a focus on dimensionality analysis and clustering methods, specifically tailored for applications in the Finance & Insurance industries. Participants will learn how to extract meaningful insights from unlabeled data, identify hidden patterns, and develop effective strategies for data preprocessing and feature engineering in finance and insurance contexts.

• Understand the fundamental principles of unsupervised learning and its applications in the Finance & Insurance industries

• Apply dimensionality reduction techniques to improve model performance and data visualization

• Implement and evaluate various clustering algorithms for customer segmentation and risk profiling

• Develop effective strategies for data preprocessing and feature engineering in unsupervised learning tasks

• Leverage unsupervised learning techniques to solve real-world problems in the Finance & Insurance domains

1. Introduction to Unsupervised Learning

• Overview of unsupervised learning and its differences from supervised learning

• Types of unsupervised learning tasks and their applications in the Finance & Insurance industries

• Challenges and considerations in unsupervised learning for finance and insurance data

• Hands-on exercises: Exploring and visualizing unlabeled financial and insurance datasets

2. Dimensionality Analysis

• The curse of dimensionality and its implications for machine learning in finance and insurance

• Principal Component Analysis (PCA) for linear dimensionality reduction

• t-SNE and UMAP for non-linear dimensionality reduction and data visualization

• Autoencoders and their applications in dimensionality reduction and anomaly detection for financial and insurance data

• Hands-on exercises: Applying dimensionality reduction techniques to financial and insurance datasets

3. Clustering Methods

• Overview of clustering and its applications in the Finance & Insurance industries

• K-means clustering and its variations (e.g., K-medoids, Mini-batch K-means)

• Hierarchical clustering (Agglomerative and Divisive) for customer segmentation and risk profiling

• Density-based clustering (DBSCAN) for anomaly detection and outlier analysis in financial and insurance data

• Evaluation metrics for clustering performance (e.g., Silhouette score, Calinski-Harabasz index)

• Hands-on exercises: Implementing and evaluating clustering algorithms on finance and insurance case studies

4. Advanced Topics and Applications

• Gaussian Mixture Models (GMM) for probabilistic clustering of financial and insurance data

• Self-Organizing Maps (SOM) for data visualization and clustering in portfolio analysis

• Combining unsupervised and supervised learning techniques (e.g., clustering for feature engineering in credit risk modeling)

• Real-world applications of unsupervised learning in the Finance & Insurance industries (e.g., fraud detection, market segmentation)

• Hands-on exercises: Developing an end-to-end unsupervised learning pipeline for a finance or insurance problem

• Solid understanding of mathematics, including linear algebra and statistics

• Proficiency in programming with Python or R

• Familiarity with basic machine learning concepts and supervised learning algorithms