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 Transportation & Logistics 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 transportation and logistics contexts.

• Understand the fundamental principles of unsupervised learning and its applications in the Transportation & Logistics industries

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

• Implement and evaluate various clustering algorithms for customer segmentation and anomaly detection

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

• Leverage unsupervised learning techniques to solve real-world problems in the Transportation & Logistics 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 Transportation & Logistics industries

• Challenges and considerations in unsupervised learning for transportation and logistics data

• Hands-on exercises: Exploring and visualizing unlabeled transportation and logistics datasets

2. Dimensionality Analysis

• The curse of dimensionality and its implications for machine learning in transportation and logistics

• 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 transportation and logistics data

• Hands-on exercises: Applying dimensionality reduction techniques to transportation and logistics datasets

3. Clustering Methods

• Overview of clustering and its applications in the Transportation & Logistics industries

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

• Hierarchical clustering (Agglomerative and Divisive) for customer segmentation and route optimization

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

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

• Hands-on exercises: Implementing and evaluating clustering algorithms on transportation and logistics case studies

4. Advanced Topics and Applications

• Gaussian Mixture Models (GMM) for probabilistic clustering of transportation and logistics data

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

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

• Real-world applications of unsupervised learning in the Transportation & Logistics industries (e.g., supply chain optimization, fleet management)

• Hands-on exercises: Developing an end-to-end unsupervised learning pipeline for a transportation or logistics 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