Statistics for Machine Learning
Why This Series
The series is aimed at people who have seen calculus and linear algebra and want to understand the statistical machinery behind ML.
Series
| # | Post | Content |
|---|---|---|
| 1 | Probability Foundations | draft |
| 2 | Random Variables & Probability Functions | draft |
| 3 | Distributions | draft |
| 4 | Descriptive Statistics | draft |
| 5 | Statistical Inference & Hypothesis Testing | draft |
| 6 | Regression | draft |
| 7 | Classification | draft |
| 8 | Model Evaluation & Selection | draft |
| 9 | Stochastic Processes & Markov Chains | draft |
What This Series Doesn't Cover
- Statistical learning theory (PAC learning, VC dimension, generalisation bounds)
- Bayesian methods in depth (priors, posteriors, MCMC)
- Multivariate normal distribution