typos and cleaning up week39

doc/src/week39/week39.do.txt

@@ -78,7 +78,7 @@ plt.show()
 
 !ec
 
-Here we have performed a rather data greedy calculation as function of the regularization parameter $\lambda$. There is no resampling here. The latter can easily be added by employing the function _RidgeCV_ instead of just calling the _Ridge_ function. For _RidgeCV_ we need to passe the array of $\lambda$ values.
+Here we have performed a rather data greedy calculation as function of the regularization parameter $\lambda$. There is no resampling here. The latter can easily be added by employing the function _RidgeCV_ instead of just calling the _Ridge_ function. For _RidgeCV_ we need to pass the array of $\lambda$ values.
 By inspecting the figure we can in turn determine which is the optimal regularization parameter.
 This becomes however less functional in the long run. 
 
@@ -137,7 +137,10 @@ print(f"R2 score: {R2(y_test,ypredictRidge)}")
 
 !ec
 
-By default the grid search function includes cross validation with five folds. The "Scikit-Learn documentation":"https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" contains more information on how to set the different parameters.
+By default the grid search function includes cross validation with
+five folds. The "Scikit-Learn
+documentation":"https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV"
+contains more information on how to set the different parameters.
 
 If we take out the random noise, running the above codes results in $\lambda=0$ yielding the best fit. 
 
@@ -149,8 +152,10 @@ An alternative to the above manual grid set up, is to use a random
 search where the parameters are tuned from a random distribution
 (uniform below) for a fixed number of iterations. A model is
 constructed and evaluated for each combination of chosen parameters.
-We repeat the previous example but now with a random search.
-
+We repeat the previous example but now with a random search.  Note
+that values of $\lambda$ are now limited to be within $x\in
+[0,1]$. This domain may not be the most relevant one for the specific
+case under study.
 
 
 !bc pycod
@@ -258,6 +263,16 @@ $p(y_i\vert x_i,\bm{\beta})(1-p(y_i\vert x_i,\bm{\beta})$, we can obtain a compa
 !et
 This defines what is called  the Hessian matrix.
 
+
+!split
+===== To be added =====
+
+We will add here an example which computes the likelihood $p_i$, sets up the gradient and the Hessian matrix.
+
+Make link with linear regression and the Hessian matrix from linear regression.
+
+
+
 !split
 ===== Solving using Newton-Raphson's method =====