Unsupervised Learning Dimensionality for Finance & Accounting Management
Course Overview:
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 & Accounting Management. 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 healthcare and life sciences contexts.
Learning Objectives:
Understand the fundamental principles of unsupervised learning and its applications in the Finance & Accounting Management
Apply dimensionality reduction techniques to improve model performance and data visualization
Implement and evaluate various clustering algorithms for segmentation and subtyping
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 & Accounting Department
Course Highlights:
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 & Accounting Management
Challenges and considerations in unsupervised learning for Finance & Accounting Management data
Hands-on exercises: Exploring and visualizing unlabeled Finance & Accounting Management datasets
2. Dimensionality Analysis
The curse of dimensionality and its implications for machine learning in Finance & Accounting Management
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 Finance & Accounting data
Hands-on exercises: Applying dimensionality reduction techniques to Finance & Accounting Management datasets
3. Clustering Methods
Overview of clustering and its applications in the Finance & Accounting Management
K-means clustering and its variations (e.g., K-medoids, Mini-batch K-means)
Hierarchical clustering (Agglomerative and Divisive) for stratification and subtyping
Density-based clustering (DBSCAN) for anomaly detection and data segmentation in Finance & Accounting Management
Evaluation metrics for clustering performance (e.g., Silhouette score, Calinski-Harabasz index)
Hands-on exercises: Implementing and evaluating clustering algorithms on Finance & Accounting Management case studies
4. Advanced Topics and Applications
Gaussian Mixture Models (GMM) for probabilistic clustering of Finance & Accounting Management data
Self-Organizing Maps (SOM) for data visualization and clustering in life sciences
Combining unsupervised and supervised learning techniques
Real-world applications of unsupervised learning in the Finance & Accounting Management
Hands-on exercises: Developing an end-to-end unsupervised learning pipeline for a Finance & Accounting problem
Prerequisites:
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