Machine Learning/Learning Center/Semi-Supervised Learning

Semi-Supervised Learning

Master techniques for learning with limited labeled data. Discover how to leverage unlabeled samples to improve model performance through generative methods, graph-based learning, co-training, and constrained clustering.

Core Fundamentals

Module 1

Understand the foundation of semi-supervised learning. Learn about labeled vs unlabeled samples, the cluster assumption and manifold assumption, and different learning paradigms including active learning, pure semi-supervised learning, and transductive learning.

Topics Covered:

Labeled vs Unlabeled Samples

Cluster Assumption

Manifold Assumption

Learning Paradigms

Active vs Semi-Supervised Learning

Generative Methods

Module 2

Master generative model-based semi-supervised learning. Learn Gaussian Mixture Models (GMM), the EM algorithm for parameter estimation, E-step and M-step calculations, and classification rules. Apply to customer segmentation and image classification.

Topics Covered:

Gaussian Mixture Models

EM Algorithm

E-step & M-step

Parameter Estimation

Customer Segmentation Examples

Semi-Supervised SVM

Module 3

Learn Transductive SVM (TSVM) for low-density separation. Master the optimization objective, algorithm steps, pseudo-label assignment, and handling class imbalance. Apply to text classification and medical diagnosis tasks.

Topics Covered:

TSVM Algorithm

Low-Density Separation

Optimization Objective

Pseudo-Label Assignment

Class Imbalance Handling

Graph-Based Learning

Module 4

Explore graph-based semi-supervised learning through label propagation. Learn graph construction with Gaussian kernels, propagation matrices, iterative label spreading, and convergence analysis. Apply to social networks and document similarity.

Topics Covered:

Graph Construction

Label Propagation

Gaussian Kernel Weights

Propagation Matrix

Convergence Analysis

Disagreement-Based Methods

Module 5

Master multi-view learning and co-training algorithms. Understand compatibility and complementarity assumptions, high-confidence sample selection, and iterative classifier improvement. Apply to web page classification and image analysis.

Topics Covered:

Multi-View Data

Co-Training Algorithm

Compatibility Assumption

High-Confidence Selection

Web Page Classification

Semi-Supervised Clustering

Module 6

Learn constrained clustering with must-link and cannot-link constraints. Master constrained k-means, seeded constrained k-means, and how to incorporate domain knowledge into clustering. Apply to customer grouping and document organization.

Topics Covered:

Must-Link Constraints

Cannot-Link Constraints

Constrained k-Means

Seeded Clustering

Domain Knowledge Integration

Suggested Learning Paths

Fundamentals Path

Start with core concepts

Core Fundamentals
Generative Methods

Advanced Methods Path

Master advanced techniques

Semi-Supervised SVM
Graph-Based Learning
Disagreement-Based Methods

Clustering Path

Focus on constrained clustering

Core Fundamentals
Semi-Supervised Clustering

Why Learn Semi-Supervised Learning?

Address Label Scarcity

In real-world applications, labeled data is expensive and time-consuming to obtain. Semi-supervised learning allows you to leverage abundant unlabeled data to improve performance.

Real-World Applications

Essential for email spam detection, medical diagnosis, text classification, social network analysis, and any domain where labeling is costly but unlabeled data is abundant.

Performance Improvement

Semi-supervised methods can significantly improve model performance compared to supervised learning alone, especially when labeled samples are limited.

Industry Standard

Widely used in industry for web page classification, image recognition, natural language processing, and customer segmentation where labeling costs are high.

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