What is covered: Basic operations (addition, subtraction, multiplication, division), fractions, powers, simple equations.

Application: Used everywhere in AI/ML for data cleaning, scaling, and calculations.

Example:
1.Image Processing: When preparing images for a neural network, pixel values  are divided to scale. This helps the model learn better.

2.Linear Equation: Predicting house prices with a simple formula  

What is covered: Groups of objects (sets), rules that assign each input to an output (functions).

Application: Organizing data, mapping features to predictions.

Example:
1.One-hot Encoding: Converting categories (like “cat”,”dog”,”fish”) into numbers for ML models using sets.
2.Function Mapping: In ML, a function maps input features (like age, income) to an output (like loan approval).

What is covered: Placing points on a grid (x, y), finding distances, slopes.

Application: Visualizing data, clustering, measuring similarity.

Example:
1. K-means Clustering: Grouping customers by age and income on a 2D plot to find similar groups.
2. Distance Formula: Calculating how close two data points are in feature space.

What is covered: Ordered lists of numbers, adding up terms.

Application: Time series analysis, summing losses in ML.

Example:
1. Stock Prediction: Using past prices (a sequence) to predict future prices.
2. Loss Calculation: Adding up errors for each prediction in a dataset.

This section includes a comprehensive evaluation covering all course topics, designed to measure understanding and mastery of key mathematical concepts presented throughout the course