Duration 3 days – 21 hrs
Overview
This course is designed to equip learners with the essential mathematical foundations needed to understand and succeed in artificial intelligence (AI) and machine learning. Participants will explore the basics of linear algebra, probability and statistics, and calculus concepts that form the core of many AI algorithms, explained in a simple and practical way.
Objectives
- Understand key concepts of linear algebra used in AI models.
- Apply basic probability and statistical techniques in AI contexts.
- Grasp fundamental calculus concepts relevant to machine learning algorithms.
- Build the confidence to pursue deeper AI and machine learning studies.
Audience
- Beginners aspiring to enter AI and machine learning fields.
- Students and professionals with limited mathematics background.
- Software engineers, analysts, and enthusiasts wanting to strengthen their AI fundamentals.
Prerequisites
- Basic algebra (addition, multiplication, simple equations).
- No advanced mathematics or programming knowledge required.
Course Content
Day 1: Linear Algebra Basics for AI
- Vectors and their operations (addition, scalar multiplication)
- Matrices and matrix operations (addition, multiplication, transpose)
- Identity and inverse matrices
- Systems of linear equations
- Application to AI: Feature representation, transformations
Day 2: Probability and Statistics Fundamentals
- Basic probability concepts (events, sample space, conditional probability)
- Random variables and probability distributions (discrete and continuous)
- Mean, median, mode, variance, and standard deviation
- Introduction to Bayes’ Theorem
- Application to AI: Predictive models, data distributions
Day 3: Calculus Basics for Machine Learning
- Functions, limits, and continuity
- Derivatives and their interpretations
- Basic rules of differentiation (sum, product, chain rule)
- Partial derivatives and gradients
- Application to AI: Optimization, gradient descent concept
Final Hands-On Activity:
- Simple exercises linking math concepts to AI scenarios (e.g., how derivatives apply in optimizing AI models).
