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

Probability Prerequisites

Basic Probability

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Probability (Part 6) - Conditional Probability

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Probability (Part 7) - Bayes' Rule

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Probability (Part 8) - More Bayes' Rule

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Introduction to Random Variables

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Probability Density Functions

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Expected Value: E(X)

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Linear Algebra Prerequisites

Introduction to Matrices

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Matrix Multiplication (Part 1)

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Matrix Multiplication (Part 2)

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Inverse Matrix (Part 1)

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Inverting Matrices (Part 2)

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Inverting Matrices (Part 3)

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Matrices to Solve a System of Equations

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Singular Matrices

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Introduction to Vectors

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Vector Dot Product and Vector Length

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Defining the Angle Between Vectors

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Cross Product Introduction

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Matrix Vector Products

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Linear Transformations as Matrix Vector Products

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Linear Transformation Examples: Scaling and Reflections

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Linear Transformation Examples: Rotations in R2

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Introduction to Projections

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Exploring the Solution Set of Ax = b

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Transpose of a Matrix

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3x3 Determinant

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Introduction to Eigenvalues and Eigenvectors

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