add ridge regression

linear_models/README.md

@@ -1,7 +1,9 @@
 # Linear Models
 The `lm.py` module implements:
 
-1. [OLS linear regression](https://en.wikipedia.org/wiki/Ordinary_least_squares) with maximum likelihood parameter estimates via the Normal equation.
+1. [OLS linear regression](https://en.wikipedia.org/wiki/Ordinary_least_squares) with maximum likelihood parameter estimates via the normal equation.
+2. [Ridge regression / Tikhonov regularization](https://en.wikipedia.org/wiki/Tikhonov_regularization)
+   with maximum likelihood parameter estimates via the normal equation.
 2. [Logistic regression](https://en.wikipedia.org/wiki/Logistic_regression) with maximum likelihood parameter estimates via gradient descent.
 3. [Bayesian linear regression](https://en.wikipedia.org/wiki/Bayesian_linear_regression) with maximum a posteriori parameter estimates via [conjugacy](https://en.wikipedia.org/wiki/Conjugate_prior#Table_of_conjugate_distributions)
     - Known coefficient prior mean and known error variance