Introduction to Machine Learning

"The field of study that gives computers the ability to learn without being explicitly programmed."
— Arthur Samuel (1959)

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Categories of Machine Learning

1. Supervised Learning
2. Unsupervised Learning
3. Recommender Systems
4. Reinforcement Learning

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1. Supervised Learning

Supervised learning involves training an algorithm on a labeled dataset, which means the data includes both input (X) and output (Y) values.

• The goal is to learn the mapping function from X → Y
• The algorithm learns from the "right answers" during training.

Regression

• Used when the output (Y) is a continuous number (from infinitely many possibilities).
• Example: Predicting house prices.

Classification

• Used when the output (Y) is categorical (from a small set of categories).
• Examples:
  • Breast cancer detection (malignant vs. benign)
  • Identifying whether a photo contains a cat or a dog

Inputs are called: Features

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2. Unsupervised Learning

In unsupervised learning, the data only contains inputs (X) and no corresponding output labels (Y).

• The algorithm must discover patterns, structures, or relationships in the data on its own.

Clustering

• A method where the algorithm groups similar examples together.
• Often used to find natural groupings in the data.

Examples of Unsupervised Learning:

• Grouping news articles (e.g., Google News)
• DNA microarray data analysis
• Customer segmentation for marketing
• Anomaly detection
• Dimensionality reduction

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