Little Bits of Artificial Intelligence

1. Algebra

• What We Need:

• Solving linear equations (e.g., 2x+3=112x + 3 = 112x+3=11)
• Understanding variables and coefficients
• Rearranging formulas

• Real-Life Analogy:

• Figuring out how many apples we can buy if 1 apple costs ₹5 and we have ₹25.

2. Coordinate Geometry

• What We Need:
  

  • Understanding how to plot points on an X-Y graph
  • Concept of a line: y=mx+by = mx + by=mx+b
  • Slope (m), intercept (b), and how they affect the line

• Real-Life Analogy:
  

  • Mapping how house price increases as size increases on a graph.

3. Matrix Basics

• What We Need:
  

  • What is a matrix and vector
  • Matrix addition and multiplication
  • Transpose of a matrix
  • Identity matrix (like multiplying by 1)
  • Inverse matrix (like division for matrices)

• Real-Life Analogy:
  

  • Think of matrices as Excel tables — rows and columns of numbers being operated together.

4. Statistics

• What We Need:
  

  • Mean (average)
  • Variance and standard deviation
  • Correlation between variables

• Real-Life Analogy:
  

  • Understanding how crime rate and house price may move together.

5. Linear Regression

• What We Need:

  • Understanding of how regression fits a line through data
  • Concept of error/residual (difference between prediction and actual)
  • Overfitting vs underfitting

• Real-Life Analogy:

  • Trying to guess a student’s marks based on their study time, and adjusting our guess if we’re too far off.

6. Loss Functions

• What We Need:

  • Mean Squared Error (MSE) and why we square the differences
  • The goal of minimizing error

• Real-Life Analogy:

  • Like minimizing the distance between our guess and the correct answer in a dart game.

7. Regularization (Conceptual Only)

• What We Need:

  • Why we want to control the size of coefficients
  • The idea of penalizing complexity

• Real-Life Analogy:

  • Telling our AI assistant: “Don’t give wild guesses just to be right — stay reasonable.”

8. Summary Table