AI class prerequisites

Here we document the prerequisite learning for the AI class.

Resources

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