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Statisticsfirst 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 Statistics (Math for AI).

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Syllabus

Curriculum Syllabus

Course chapter sequence roadmap and learning outcomes.

Statistics (Math for AI)

1. Descriptive Statistics

1.1 Summarizing Data
1.2 Sampling

2. Parameter Estimation

2.1 Maximum Likelihood
2.2 MAP Estimation
2.3 Properties of Estimators

3. Inference and Testing

3.1 Confidence Intervals
3.2 Hypothesis Testing
3.3 A/B Testing for ML

4. Sampling and Monte Carlo

4.1 Monte Carlo Estimation
4.2 Importance Sampling
4.3 Markov Chain Monte Carlo
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.

scienceOpen Interactive Laboratories→Board MCQs & Past Papers