Machine Learning 🤖

AI

AI is the field of study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals

ML

ML is the study of computer algorithms that improve automatically through experience.

• ML is Subset of AI
• Learning from data
• Improving performance (P) with experience(E) while performing Task (T)

Older definition -- Arthur Samuel (1959)

The field of study that gives computers the ability to learn without being explicitly programmed.

Modern definition -- Tom Mitchell (1998)

A program learns from:

• E (Experience)- User-labeled emails
• T (Task) - Classify emails as spam or not spam
• P (Performance measure) - Fraction of correctly classified emails

If performance on task T, measured by P, improves with experience E, then it is learning.

Use Cases

ML is powerful when:

• Handling problems too complex to hard-code
• Finding hidden patterns in large datasets

1. Large Datasets Exist

• Web Analytics data
• Medical records
• Biological data

2. Problems Are Hard to Hand-Code

• Autonomous Drive
• Handwriting recognition
• NLP(Natural Language Processing)
• Computer vision

3. Self-Customizing Systems

• Amazon recommendations
• Netflix recommendations

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ML Algos types

1. Supervised Learning

You give the algorithm input data and the correct outputs (“right answers”), and it learns to predict outputs for new inputs.

Training set:

• You are given labeled data.

$(x^(1), y^(1)), (x^(2), y^(2)), ..., (x^(m), y^(m))$

Where:

• $x^{(i)}$ is the input (features)

• $y^{(i)}$ is the correct output (label)

• Goal: Learn a function that maps inputs → outputs.

• Example: Find a decision boundary separating positive and negative examples.

In supervised learning, we know the correct answers and train a model to predict them.

Example

• Spam filtering with labeled emails
• Diabetes classification with labeled patients
• Cancer Type Prediction

Types

1.1 Regression

Regression means predicting a continuous value output.

Example: Housing Price Prediction (Regression)

Predict the price of a house based on its size.

• Feature (x): House size (square feet)
• Output (y): Price (continuous value)

We are given historical data:

Size (sq ft)	Price ($)
1000	200000
1500	300000
2000	400000

The algorithm may:

• Fit a straight line (Linear Regression)
• Fit a quadratic curve (Polynomial Regression)

Different models may produce different predictions.

1.2 Classification

Classification means predicting a discrete category as output

• We train using past labeled examples.
• Only specific categories allowed as output (0 or 1)

Example: Breast Cancer Detection (Classification)

What is the probability this tumor is malignant?

• Malignant (1)
• Benign (0)

Using One Feature

• Feature: Tumor size
• Output: 0 or 1

Even if there are multiple categories:

• 0 → No cancer
• 1 → Type 1 cancer
• 2 → Type 2 cancer
• 3 → Type 3 cancer

It is still classification because the output is from a finite set of categories.

Multiple Features

In real problems, we use more than one feature:

• Tumor size
• Age
• Clump thickness
• Uniformity of cell size
• Uniformity of cell shape

The algorithm learns a decision boundary that separates categories.

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2. Unsupervised Learning

There are no labeled outputs. The system tries to find structure in the data.

Training set:

unlabeled data.

• No labels
• No correct answers
• No predefined categories

$x^(1), x^(2), ..., x^(m)$

Where:

• $x^{(i)}$ is the input (features)
• There are no y labels.

Goal

Discover hidden structure in the data.

"Here is the data. Can you find structure in it?"

We do not tell the algorithm what the correct output is.
We ask it to find patterns on its own.

• Discovers hidden structure
• Common task: Clustering
• Advanced example: Cocktail Party Problem

2.1 Clustering

The algorithm automatically groups similar data points together.

• Used to find patterns

We are not told:

• How many groups exist
• What the groups represent
• Which example belongs to which group

The algorithm discovers that on its own.

Example

• Given market data Identify patterns in buying behavior
• Given news articles data find topics
• Given Data Centers logs find machines that frequently work together
• Given Social Network data find groups or communities
• Given customer data find Market Segmentation
• Given Astronomical data find galaxies

2.2 Blind Source Separation

Separating mixed signals into original independent components.

The Cocktail Party Problem

Given only the mixed recordings:

• Detect that multiple sources exist
• Separate them into independent signals
• Recover the original voices

No labels are given:

• We do not tell the algorithm what each voice sounds like
• It discovers structure in the signal

Separate the original voices from mixed signals.

Difference Between Supervised and Unsupervised Learning

Aspect	Supervised Learning	Unsupervised Learning
Data	Labeled data (input + correct output)	Unlabeled data (input only)
Goal	Learn mapping from input → output	Discover hidden structure or patterns
Output Type	Continuous (regression) or discrete (classification)	Clusters, groups, latent structure
Example Problem	House price prediction	Customer segmentation
Example Problem	Spam detection	Grouping news articles
Human Guidance	Requires correct answers during training	No correct answers provided
Typical Tasks	Regression, Classification	Clustering, Dimensionality Reduction
Evaluation	Compare predictions with true labels	Evaluate structure quality (e.g., cohesion, separation)
Use Case	When you know what you want to predict	When you want to explore unknown patterns

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3. Reinforced Learning

4. Recommender System