Unsupervised Learning and Dimensionality Reduction

Introduction

What this guide covers

Introduction Introduction Advanced Treatise on Artificial Intelligence

Overview

Topics covered

• Introduction
• Unsupervised Learning and Inference of High Dimensionality Manifolds
• Theoretical Foundations of Unsupervised Learning
• Learning Space Taxonomy
• The Curse of Dimensionality: Geometric Analysis
• Measurement Concentration in Hyperspheres and Hypercubes

Related concepts

Main connections

• Unsupervised learning & clustering
• Linear algebra & matrices
• Variance & standard deviation
• Principal component analysis (PCA)
• Mean & expected value
• Normal (Gaussian) distribution

Compatible notation

Mathematical sample in LaTeX

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