What is covered: Vectors, matrices, operations like addition and multiplication.

Application: Data representation, image processing, neural networks.

Example:
1. Image as Matrix: A grayscale image is a matrix of pixel values. Neural networks process these matrices to recognize objects.
2. Matrix Multiplication: Used to combine weights and inputs in every layer of a neural network.

What is covered: Studying change (derivatives), finding minimum/maximum values.

Application: Training models by minimizing error (loss), adjusting weights.

Example:
1. Gradient Descent: The process of finding the best model parameters by moving in the direction that reduces error, like rolling a ball downhill.
2. Backpropagation: Calculating how much each weight in a neural network should change to improve predictions.

What is covered: Measuring uncertainty, analyzing data, making predictions.

Application: Predicting outcomes, evaluating models, handling randomness.

Example:
1. Spam Detection: Using probability to decide if an email is spam based on words it contains.
2. Model Evaluation: Calculating accuracy, precision, and recall to see how well a model performs.

What is covered: Logic, graphs, counting, combinations.

Application: Social networks, recommendation systems, logical reasoning.

Example:
1.Friend Recommendations: Using graph theory to suggest new friends on social media.
2.Counting Possibilities: Calculating how many ways a password can be formed.

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