AI class prerequisites

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Here we document the prerequisite learning for the AI class.

Resources

Probability Prerequisites

Linear Algebra Prerequisites

Probability Prerequisites

Basic Probability

Probability (Part 6) - Conditional Probability

Probability (Part 7) - Bayes' Rule

Probability (Part 8) - More Bayes' Rule

Introduction to Random Variables

Probability Density Functions

Expected Value: E(X)

Linear Algebra Prerequisites

Introduction to Matrices

Matrix Multiplication (Part 1)

Matrix Multiplication (Part 2)

Inverse Matrix (Part 1)

Inverting Matrices (Part 2)

Inverting Matrices (Part 3)

Matrices to Solve a System of Equations

Singular Matrices

Introduction to Vectors

Vector Dot Product and Vector Length

Defining the Angle Between Vectors

Cross Product Introduction

Matrix Vector Products

Linear Transformations as Matrix Vector Products

Linear Transformation Examples: Scaling and Reflections

Linear Transformation Examples: Rotations in R2

Introduction to Projections

Exploring the Solution Set of Ax = b

Transpose of a Matrix

3x3 Determinant

Introduction to Eigenvalues and Eigenvectors