correcting typos in log reg

doc/src/LogisticRegression/LogReg.do.txt

@@ -11,7 +11,7 @@ coefficients of a functional fit (say a polynomial) in order to be
 able to predict the response of a continuous variable on some unseen
 data. The fit to the continuous variable $y_i$ is based on some
 independent variables $\hat{x}_i$. Linear regression resulted in
-analytical expressions (in terms of matrices to invert) for several
+analytical expressions for standard ordinary Least Squares or Ridge regression (in terms of matrices to invert) for several
 quantities, ranging from the variance and thereby the confidence
 intervals of the parameters $\hat{\beta}$ to the mean squared
 error. If we can invert the product of the design matrices, linear
@@ -40,7 +40,7 @@ failure etc.
 Logistic regression will also serve as our stepping stone towards neural
 network algorithms and supervised deep learning. For logistic
 learning, the minimization of the cost function leads to a non-linear
-equation in the parameters $\hat{\beta}$. The optmization of the problem calls therefore for minimization algorithms. This forms the bottle neck of all machine learning algorithms, namely how to find reliable minima of a multi-variable function. This leads us to the family of gradient descent methods. The latter are the working horses of basically all modern machine learning algorithms. 
+equation in the parameters $\hat{\beta}$. The optimization of the problem calls therefore for minimization algorithms. This forms the bottle neck of all machine learning algorithms, namely how to find reliable minima of a multi-variable function. This leads us to the family of gradient descent methods. The latter are the working horses of basically all modern machine learning algorithms. 
 
 We note also that many of the topics discussed here 
 regression are also commonly used in modern supervised Deep Learning