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Probabilityfirst principles.

MATH FOR AI • FROM ZERO TO MODELS.

Chapter-by-chapter theory notes, step-by-step textbook exercises, video lectures, and SLO-aligned board examination prep for Probability (Math for AI).

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Syllabus

Curriculum Syllabus

Course chapter sequence roadmap and learning outcomes.

Probability (Math for AI)

1. Probability Basics

1.1 Sample Spaces, Events & Probability
1.2 Random Variables
1.3 Sum Rule, Product Rule & Bayes

2. Expectation and Independence

2.1 Expectation & Variance
2.2 Covariance & Correlation
2.3 Independence

3. Common Distributions

3.1 Discrete Distributions
3.2 The Gaussian
3.3 Working with Gaussians
3.4 Change of Variables

4. Conjugacy and the Exponential Family

4.1 Conjugate Priors
4.2 Exponential Family

5. Information Theory Basics

5.1 Entropy
5.2 Cross-Entropy & KL Divergence
5.3 Mutual Information
INTERACTIVE LAB • 18 STEM WORKSTATIONS

Bridge High-School Science to Computational AI

Connect textbook physics, mathematics, and computing to real-world artificial intelligence models and symbolic solvers.

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