small typos

doc/src/week37/exercisesweek37.do.txt

@@ -17,7 +17,7 @@ o Scale the data properly
 We start with a very simple function
 !bt
 \[
-\f(x)= 2-x+5x^2,
+f(x)= 2-x+5x^2,
 \]
 !et
 
@@ -88,10 +88,10 @@ print("Closed-form OLS coefficients:", theta_closed_form)
 !ec
 
 This computes the Ridge and OLS regression coefficients directly. The identity
-matrix $I$ has the same size as $X^T X$. It adds $\lambda$ to the diagonal of $X^T X for Ridge regression. We
+matrix $I$ has the same size as $X^T X$. It adds $\lambda$ to the diagonal of $X^T X$ for Ridge regression. We
 then invert this matrix and multiply by $X^T y$. The result
 for $\bm{\theta}$  is a NumPy array of shape (n$\_$features,) containing the
-fitted parameters $\bm{\theta}$..
+fitted parameters $\bm{\theta}$.
 
 === 3a) ===
 Finalize, in the above code, the OLS and Ridge regression determination of the optimal parameters $\bm{\theta}$.