1. Definition

Semi-supervised Learning is a type of Machine Learning that uses a small amount of labeled data along with a large amount of unlabeled data to train models.

It combines the strengths of Supervised Learning (accuracy from labeled data) and Unsupervised Learning (finding patterns in unlabeled data).

👉 Example: In medical imaging, only a few scans are labeled by doctors, but thousands of unlabeled scans exist. Semi-supervised learning can use both to build better models.

2. Why It’s Needed

• Labeling data is expensive and time-consuming (e.g., tagging millions of images, transcribing hours of speech).
• Unlabeled data is abundant but cannot be directly used in supervised training.
• Semi-supervised learning bridges the gap by leveraging both.

3. How It Works

1. Start with a small labeled dataset to train an initial model.
2. Use the model to assign pseudo-labels to unlabeled data.
3. Retrain the model with both labeled and pseudo-labeled data.
4. Iterate until accuracy improves.

4. Common Techniques

• Self-training – Model predicts labels for unlabeled data and retrains itself.
• Co-training – Two models label data for each other.
• Graph-based Methods – Represent data as a graph and spread label information.
• Semi-supervised GANs – Use adversarial training with few labeled samples.

5. Applications

• Healthcare – Diagnosing diseases from partially labeled medical scans.
• Speech Recognition – Using few transcribed recordings + many raw audio files.
• Web Content Classification – Categorizing websites with few labeled pages.
• Natural Language Processing (NLP) – Sentiment analysis with limited labeled text.
• Cybersecurity – Detecting threats with limited labeled attack data.

6. Advantages

✅ Reduces labeling cost & effort
✅ Achieves higher accuracy than purely unsupervised learning
✅ Useful when labeled data is scarce but unlabeled data is plentiful

7. Challenges

⚠️ Pseudo-labeling errors can propagate and mislead the model
⚠️ More complex than supervised/unsupervised approaches
⚠️ Requires careful tuning to balance labeled vs. unlabeled data

8. Popular Algorithms

• Semi-supervised SVM (Support Vector Machines)
• Label Propagation / Label Spreading
• Self-training Neural Networks
• Semi-supervised GANs

✅ In short: Semi-supervised Learning = Small labeled dataset + Large unlabeled dataset → Better model performance.