🧮Foundations
📊
Probability & Statistics
Master probabilistic thinking and statistical inference for understanding ML models
🌱
Beginner
BeginnerBuild probabilistic intuition
What to Learn
- •Probability basics: events, outcomes, rules
- •Conditional probability and Bayes theorem
- •Common distributions (Gaussian, Bernoulli, Uniform)
- •Expected value and variance
- •Central Limit Theorem intuition
Resources
- 📚StatQuest with Josh Starmer (YouTube)
- 📚Khan Academy Statistics
- 📚Seeing Theory (visual probability)
🌿
Intermediate
IntermediateApply statistics to ML
What to Learn
- •Maximum Likelihood Estimation (MLE)
- •Bayesian inference basics
- •Hypothesis testing and p-values
- •Information theory: entropy, KL divergence
- •Monte Carlo methods
Resources
- 📚All of Statistics by Wasserman
- 📚Pattern Recognition and ML Chapter 2
- 📚Probabilistic Graphical Models course
🌳
Advanced
AdvancedDeep probabilistic modeling
What to Learn
- •Variational inference
- •Markov Chain Monte Carlo (MCMC)
- •Gaussian processes
- •Probabilistic programming (PyMC, Stan)
- •Causal inference basics
Resources
- 📚Machine Learning: A Probabilistic Perspective
- 📚Bayesian Data Analysis by Gelman
- 📚VAE and diffusion model papers