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 Healthcare & Life Sciences 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 healthcare and life sciences contexts.

• Understand the fundamental principles of unsupervised learning and its applications in the Healthcare & Life Sciences industries

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

• Implement and evaluate various clustering algorithms for patient segmentation and disease 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 Healthcare & Life Sciences domain

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 Healthcare & Life Sciences industries

• Challenges and considerations in unsupervised learning for healthcare and life sciences data

• Hands-on exercises: Exploring and visualizing unlabeled healthcare datasets

2. Dimensionality Analysis

• The curse of dimensionality and its implications for machine learning in healthcare and life sciences

• 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 healthcare data

• Hands-on exercises: Applying dimensionality reduction techniques to healthcare and life sciences datasets

3. Clustering Methods

• Overview of clustering and its applications in the Healthcare & Life Sciences industries

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

• Hierarchical clustering (Agglomerative and Divisive) for patient stratification and disease subtyping

• Density-based clustering (DBSCAN) for anomaly detection and data segmentation in healthcare

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

• Hands-on exercises: Implementing and evaluating clustering algorithms on healthcare and life sciences case studies

4. Advanced Topics and Applications

• Gaussian Mixture Models (GMM) for probabilistic clustering of healthcare data

• Self-Organizing Maps (SOM) for data visualization and clustering in life sciences

• Combining unsupervised and supervised learning techniques (e.g., clustering for feature engineering in disease prediction)

• Real-world applications of unsupervised learning in the Healthcare & Life Sciences industries (e.g., patient phenotyping, drug discovery)

• Hands-on exercises: Developing an end-to-end unsupervised learning pipeline for a healthcare or life sciences 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