Little Bits of Artificial Intelligence

1. What Is a Learning Algorithm?

A learning algorithm is like a smart rule-making engine. It looks at data, finds patterns, and builds a rule or model to make decisions or predictions.

Imagine this:
Suppose a kid learning how to guess the price of a house just by looking at its features—like size, location, and number of rooms.

At first, he is just guessing.But over time, he looks at lots of real examples (data), and his guesses get better. That’s what a learning algorithm does!

Let’s break it down simply:

1. Input Data (Experience) : Just like we learn from practice tests, the algorithm learns from data.

Example: A table with house prices, sizes, locations, and number of rooms.

2. Pattern Finder : It tries to connect inputs to outputs.

Like saying: “Whenever the house has more rooms, the price goes up.”It uses basic math operations (like multiplication, averaging, etc.) to figure this out.

3. Guess → Check → Improve : It guesses a rule (e.g., “Each room adds ₹50,000 to the price”).

Then it checks how wrong it was (this is the error).It uses that error to adjust the rule and try again.(This part uses a method called “minimizing error”.)

4. Repeat :It repeats this process many times until the guesses become very close to correct.

What Math Do You Need to Understand This?

a.Basic algebra (addition, subtraction, multiplication)
b.Graphs (how a line fits points)
c.Averages
d.Understanding patterns

Final Output: A Model -> Once it has learned, the algorithm produces a model. This model can now be used to make new predictions without seeing the answers before.

Real-life Analogy

Imagine teaching a child to identify fruits: If it’s red, round, and shiny, it’s likely an apple.

At first, the child may confuse apples and tomatoes.But after seeing enough examples, they’ll learn the differences.

B. Example:

Suppose a student is trying to figure out how many hours needed to study to score well in an exam.

At first, he is just guessing:

“If I study 2 hours, maybe I’ll get 40 marks.”

“If I study 5 hours, maybe I’ll get 75.”

But over time, he gathers data from his past exams or from his classmates:

“I studied 3 hours → got 50 marks”

“I studied 6 hours → got 80 marks”

Soon, he starts to see a pattern. This pattern becomes your prediction rule.That’s exactly what a learning algorithm does!

Steps in Simple Words:

1. Input Data (Experience)

Suppose a table is given of past study hours and exam scores.

2. Finding Patterns

It finds a rule like: “Every extra hour of study gives +10 marks”

So, maybe: Score = 10 × (Study Hours) + 15

3. Guess → Check → Improve

It guesses a formula.Then it checks how wrong the guess is (called error).Then it tweaks the formula to improve it.

4. Repeat: This process is repeated many times to reduce the error as much as possible.

Final Output: A Prediction Model -> Now we have a model that can answer:

“If I study 7 hours, what’s my likely score?”

The model says:

Score = 10 × 7 + 15 = 85