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Courses/Prob & Stats

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.

Intermediate14 weeks · 14 lessons
Your progress00 / 14 · 0%
Lessons
W01Week 1: Probability Spaces and EventsFoundationsW02Week 2: Random Variables and DistributionsFoundationsW03Week 3: Expectation, Variance, and CovarianceFoundationsW04Week 4: Conditioning and IndependenceFoundationsW05Week 5: Bayes' Rule and Generative ClassificationFoundationsW06Week 6: Multivariate GaussiansFoundationsW07Week 7: Laws of Large Numbers and the CLTInferenceW08Week 8: Point Estimation — Maximum LikelihoodInferenceW09Week 9: Bayesian Estimation and MAPInferenceW10Week 10: Bias, Variance, and UncertaintyInferenceW11Week 11: Information Theory for MLML interfaceW12Week 12: Exponential Families (lite)ML interfaceW13Week 13: Graphical Models (lite)ML interfaceW14Week 14: Capstone — Probabilistic Language of MLCapstone