diff --git a/lectures/2020-04-30.tex b/lectures/2020-04-30.tex
index d87289b..2b0afa2 100644
--- a/lectures/2020-04-30.tex
+++ b/lectures/2020-04-30.tex
@@ -26,7 +26,7 @@
 
     The performance index can be redefined:
     \[
-        \tilde{J}_H (\theta) = \frac{1}{H} \sum_{i=1}^H \gamma_i \left(W(e^{j\omega_i};\theta) - \frac{\hat{B}_i}{A_i}e^{j\hat{\phi_i}}\right)^2
+        \tilde{J}_H (\theta) = \frac{1}{H} \sum_{i=1}^H \lambda_i \left(W(e^{j\omega_i};\theta) - \frac{\hat{B}_i}{A_i}e^{j\hat{\phi_i}}\right)^2
     \]
 
     Another \emph{trick}: more dense $\omega_i$ spacing in the frequency region of special interest (not really used).
diff --git a/lectures/2020-05-05.tex b/lectures/2020-05-05.tex
index 566013f..1d9fad4 100644
--- a/lectures/2020-05-05.tex
+++ b/lectures/2020-05-05.tex
@@ -132,7 +132,7 @@ \subsection{Exogenous input}
     \end{tikzpicture}
 \end{figure}
 
-Notice that $K(t)$ remains the same because $P(t)$ is the covariance of the prediction error on $x(t)$ and remains the same because $Gu(t)$ introduce any additional noise or uncertainties to the system.
+Notice that $K(t)$ remains the same because $P(t)$ is the covariance of the prediction error on $x(t)$ and remains the same because $Gu(t)$ doesn't introduce any additional noise or uncertainties to the system.
 $Gu(t)$ is a totally known (deterministic) signal.
 
 \subsection{Multi-step Prediction}
diff --git a/lectures/2020-05-14.tex b/lectures/2020-05-14.tex
index ef1a5c5..cfb385f 100644
--- a/lectures/2020-05-14.tex
+++ b/lectures/2020-05-14.tex
@@ -100,7 +100,7 @@ \section{Non-linear Systems}
 \begin{figure}[H]
     \centering
     \begin{tikzpicture}[node distance=2cm,auto,>=latex']
-        \node[int,double border,align=center] at (0,0) (n) {non-lin\\dyn T.I. sys};
+        \node[int,dashed border,align=center] at (0,0) (n) {non-lin\\dyn T.I. sys};
         \draw[<-,transform canvas={yshift=0.3cm}] (n) -- ++(-2,0) node[left] {$u(t)$};
         \draw[<-,transform canvas={yshift=-0.3cm}] (n) -- ++(-2,0) node[left] {$y(t)$};
         \draw[->] (n) -- ++(2,0) node[right] {$\hat{x}(t)$};
@@ -308,7 +308,7 @@ \section{Non-linear Systems}
 \begin{figure}[H]
     \centering
     \begin{tikzpicture}[node distance=2cm,auto,>=latex']
-        \node[int, dashed border, minimum width=1.5cm, minimum height=3cm] at (0,0) (sys) {};
+        \node[int, double border, minimum width=1.5cm, minimum height=3cm] at (0,0) (sys) {};
         \node[int, dashed border, minimum height=3cm] at (4,0) (f) {$f(\cdot,\theta)$};
 
         \draw[<-,transform canvas={yshift=0.5cm}] (sys) -- ++(-2cm,0) node[left] {$u(t)$};