Machine Learning | Module 5: Unsupervised Learning & Dimensionality Reduction (Weeks 10) by Prakhar Chauhan | Learn Smarter
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Knowledge
Module 5: Unsupervised Learning & Dimensionality Reduction (Weeks 10)

The focus shifts to unsupervised learning techniques involving clustering and dimensionality reduction. Key concepts include Gaussian Mixture Models (GMMs) for clustering, various anomaly detection algorithms, and mastering Principal Component Analysis (PCA) for reducing dimensionality. Understanding the differences between feature selection and feature extraction further enhances practical application in data analysis.

Sections

  • 1

    Module 5: Unsupervised Learning & Dimensionality Reduction

    This module explores advanced unsupervised learning methods, focusing on clustering with Gaussian Mixture Models (GMMs), anomaly detection algorithms, and dimensionality reduction techniques including PCA and t-SNE.

    Learning Practice

  • 1.1

    Module Objectives (For Week 10)

    This section outlines the objectives for Week 10's focus on advanced unsupervised learning techniques and dimensionality reduction.

    Learning Practice

  • 2

    Week 10: Advanced Unsupervised & Dimensionality Reduction

    This section focuses on advanced unsupervised learning methods, including Gaussian Mixture Models, anomaly detection techniques, and dimensionality reduction through PCA and t-SNE.

    Learning Practice

  • 2.1

    Gaussian Mixture Models (Gmm): A Probabilistic Approach To Clustering

    Gaussian Mixture Models (GMMs) provide a flexible probabilistic framework for clustering, distinguishing them from K-Means by allowing soft assignments and accommodating various cluster shapes.

    Learning Practice

  • 2.2

    Anomaly Detection: Identifying The Unusual

    Anomaly detection focuses on identifying rare items or events that significantly differ from the majority of data, crucial for tasks like fraud detection.

    Learning Practice

  • 2.3

    Dimensionality Reduction: Simplifying Complexity

    This section covers various techniques for dimensionality reduction in high-dimensional datasets, emphasizing methods such as PCA and t-SNE.

    Learning Practice

  • 2.4

    Feature Selection Vs. Feature Extraction: Strategic Data Reduction

    This section contrasts feature selection and feature extraction, emphasizing their unique methodologies and strategic applications in reducing data dimensionality.

    Learning Practice

  • 3

    Lab: Exploring Advanced Unsupervised Learning And Applying Pca For Data Reduction

    This section covers advanced unsupervised learning techniques including Gaussian Mixture Models (GMMs), Anomaly Detection, and Principal Component Analysis (PCA), culminating in a hands-on lab exercise.

    Learning Practice

  • 3.1

    Lab Objectives

    This section outlines the objectives for the lab focused on advanced unsupervised learning techniques and dimensionality reduction.

    Learning Practice

  • 3.2

    Activities

    This section focuses on various activities students will engage in to deepen their understanding of advanced unsupervised learning techniques, including clustering, anomaly detection, and dimensionality reduction.

    Learning Practice

References

Untitled document (24).pdf

Class Notes

Memorization

What we have learnt

  • Unsupervised learning helps...
  • Gaussian Mixture Models pro...
  • Dimensionality reduction te...

Final Test

Revision Tests