Education

LECTURE OVERVIEW
Introduction to Machine Learning - Supervised vs Unsupervised Learning.
Professor introduces fundamental concepts in machine learning, explains
the difference between supervised and unsupervised learning approaches,
and demonstrates practical applications with real-world examples.

KEY CONCEPTS

Supervised Learning
Definition: A machine learning approach where the model is trained on
labeled data - input examples paired with correct output labels. The
model learns to map inputs to outputs based on these examples.

Common algorithms: Linear regression, logistic regression, decision
trees, neural networks, support vector machines.

Unsupervised Learning
Definition: Machine learning approach where the model finds patterns in
unlabeled data without being told what to look for. The algorithm
identifies hidden structures and relationships in the data.

Common algorithms: K-means clustering, hierarchical clustering, principal
component analysis (PCA), anomaly detection.

EXAMPLES & CASE STUDIES

Email Spam Detection (Supervised Learning)
Train model on thousands of emails labeled as "spam" or "not spam."
Model learns patterns that distinguish spam from legitimate emails.
Accuracy: Typically 95-99% with enough training data.

Customer Segmentation (Unsupervised Learning)
Analyze customer purchase behavior without predefined categories.
Algorithm automatically groups customers with similar buying patterns.
Result: Discovered 5 distinct customer segments for targeted marketing.

Medical Diagnosis (Supervised Learning)
Train on patient data with known diagnoses.
Model learns to predict diseases based on symptoms and test results.
Note: Requires large, high-quality labeled datasets for accuracy.

IMPORTANT FORMULAS

Linear Regression: y = mx + b
Where y is the predicted output, m is the slope, x is the input, and
b is the y-intercept.

Mean Squared Error (MSE): MSE = (1/n) Σ(y_actual - y_predicted)²
Used to measure model accuracy - lower MSE indicates better predictions.

KEY TAKEAWAYS

• Supervised learning requires labeled data, unsupervised does not
• Choose supervised when you have clear target outcomes to predict
• Choose unsupervised when exploring data to discover patterns
• Quality and quantity of training data directly impacts model performance
• Real-world applications often combine both approaches
• Bias in training data leads to biased models - always validate results

QUESTIONS RAISED

Q: How much data is needed to train a supervised model?
A: Depends on problem complexity. Simple problems: hundreds of examples.
Complex problems (like image recognition): thousands to millions.

Q: Can unsupervised learning be used for predictions?
A: Not directly for predictions, but for discovering patterns that can
inform predictions. Example: segment customers, then predict behavior
per segment.

Q: What's more accurate, supervised or unsupervised?
A: Supervised is generally more accurate for specific tasks when you
have good labeled data. Unsupervised is better for exploration and
finding unknown patterns.

RELATED TOPICS MENTIONED
- Reinforcement learning (covered in next lecture)
- Deep learning and neural networks (Week 5)
- Feature engineering and data preprocessing (Lab 2)
- Overfitting and model validation (Next lecture)