Probability & Statistics

From the axioms to inference, regression and Bayes.

Syllabus

  1. Probability Foundations

    1. What Is Probability? — Sample Spaces, Events & the Axioms
    2. Conditional Probability, Independence & Bayes' Theorem
    3. Random Variables — Expectation & Variance
    4. Key Distributions

  2. Randomness at Scale

    1. Joint Distributions
    2. Transformations & MGFs
    3. LLN & the Central Limit Theorem
    4. Sampling Distributions

  3. Statistical Inference

    1. Point Estimation & MLE
    2. Confidence Intervals
    3. Hypothesis Testing
    4. Linear Regression & Least Squares

  4. Models & Bayes

    1. Bayesian Inference
    2. Capstone — Resampling, ANOVA & the Road to ML