Little Bits of Artificial Intelligence

1. Distance Calculation (Geometry & Algebra)

• Understand how to compute Euclidean Distance
  

  distance=√(x1−x2)2+(y1−y2)2+…

• This is the heart of KNN — to find the “nearest” data points.

2. Coordinate System / Vectors (Geometry Basics)

• Know how to visualize points in 2D or 3D space.
• Understand a data point as a vector (e.g., [Size, Bedrooms, Location]).

3. Averages (Arithmetic Mean)

• You need to calculate the mean of K neighbor values to predict.
• Example:
  

  Prediction=y1+y2+⋯+yk / K

4. Sorting and Ranking (Logical Thinking)

• After calculating distances, you sort to find the closest ones.

5. Basic Set Operations / Lists

• Concept of collecting data, comparing elements, and storing values (important for logic in implementation).

6. Scaling and Normalization

• Basic idea of min-max scaling:
  

  xnorm=x−min(x) / max(x)−min(x)

• Helps in handling features with different ranges fairly.

7. Square Roots & Exponents

• Because Euclidean distance uses squares and square roots.