Unsupervised Learning Dimensionality
Course Overview:
This course delves into the exciting world of Unsupervised Learning, a powerful branch of Artificial Intelligence (AI) crucial for Supply Chain Management (SCM) professionals. You'll explore techniques for dimensionality reduction and data clustering, enabling you to uncover hidden patterns and make data-driven decisions in your supply chains.
Learning Objectives:
Explain the concept of Unsupervised Learning and its applications in SCM.
Grasp dimensionality reduction techniques like Principal Component Analysis (PCA).
Master clustering algorithms like K-Means Clustering and Hierarchical Clustering.
Apply these techniques to analyze real-world SCM data (e.g., customer segmentation, demand patterns).
Interpret the results of dimensionality reduction and clustering for informed decision-making.
Course Highlights:
1. Unsupervised Learning and Dimensionality Reduction
What is Unsupervised Learning? Contrasting it with Supervised Learning.
The "Curse of Dimensionality" and its challenges in data analysis.
Introduction to Dimensionality Reduction Techniques.
Principal Component Analysis (PCA): Understanding the underlying concepts, performing PCA on SCM data (hands-on exercise).
2. Clustering Algorithms and Applications in SCM
Introduction to Clustering: K-Means Clustering explained (algorithm, distance metrics).
Applying K-Means Clustering to SCM data (customer segmentation, product grouping) (hands-on exercise).
Exploring Hierarchical Clustering methods.
Case studies: How unsupervised learning empowers better decision-making in supply chains.
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