3.1 Core Principles

This section introduces the essential theoretical foundations required for understanding and implementing AI systems. It serves as a primer on key mathematical and computational concepts:

3.1.1 What is an Algorithm?

Explains the concept of algorithms as step-by-step procedures used to solve problems or perform computations, fundamental to all AI and programming logic.


3.1.2 Data Structures for AI

Introduces basic data structures such as arrays, lists, trees, graphs, and hash tables, highlighting how they support data organization and efficient AI processing.


3.1.3 Probability and Statistics Basics

Covers essential statistical concepts like distributions, mean, variance, conditional probability, and Bayes’ theorem, which are crucial for modeling uncertainty and learning from data.


3.1.4 Linear Algebra and Vectors

Discusses vectors, matrices, and their operations, which are core tools in representing data and performing computations in machine learning and deep learning.


3.1.5 Introduction to Optimization

Introduces optimization as the process of adjusting parameters to minimize (or maximize) an objective function, forming the mathematical backbone of model training in AI.


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