ePOWERAI
CoursesProb & Stats

Foundations

Probability & Statistics

Probability, conditioning, and estimation for machine learning — from random variables and Bayes to MLE/MAP, uncertainty, and the language of modern generative and RL models.

4 modules · 14 lessons · runs in your browser · no account

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6 lessons here are on the Guided Foundations Path· Steps 02-07

Course contents

Foundations

  1. W01Week 1: Probability Spaces and Events
  2. W02Week 2: Random Variables and Distributions
  3. W03Week 3: Expectation, Variance, and Covariance
  4. W04Week 4: Conditioning and Independence
  5. W05Week 5: Bayes' Rule and Generative Classification
  6. W06Week 6: Multivariate Gaussians

Inference

  1. W07Week 7: Laws of Large Numbers and the CLT
  2. W08Week 8: Point Estimation — Maximum Likelihood
  3. W09Week 9: Bayesian Estimation and MAP
  4. W10Week 10: Bias, Variance, and Uncertainty

ML interface

  1. W11Week 11: Information Theory for ML
  2. W12Week 12: Exponential Families (lite)
  3. W13Week 13: Graphical Models (lite)

Capstone

  1. W14Week 14: Capstone — Probabilistic Language of ML