diff --git a/notes/sections/7.md b/notes/sections/7.md
index b6ef97b..5ebb290 100644
--- a/notes/sections/7.md
+++ b/notes/sections/7.md
@@ -36,11 +36,11 @@ samples were handled as matrices of dimension $n$ x 2, where $n$ is the number
 of points in the sample. The library `gsl_matrix` provided by GSL was employed
 for this purpose and the function `gsl_ran_bivariate_gaussian()` was used for
 generating the points.  
-An example of the two samples is shown in @fig:fisher_points.
+An example of the two samples is shown in @fig:points.
 
 ![Example of points sorted according to two Gaussian with
 the given parameters. Noise points in pink and signal points
-in yellow.](images/fisher-points.pdf){#fig:fisher_points}
+in yellow.](images/points.pdf){#fig:points}
 
 Assuming not to know how the points were generated, a model of classification
 must then be implemented in order to assign each point to the right class
@@ -154,6 +154,7 @@ the Cholesky method, already discussed in @sec:MLM.
 Lastly, the matrix-vector product was computed with the `gsl_blas_dgemv()`
 function provided by GSL.
 
+
 ### The threshold
 
 The cut was fixed by the condition of conditional probability being the same
@@ -198,7 +199,8 @@ this case were the weight vector and the position of the point to be projected.
 ![Gaussian of the samples on the projection
   line.](images/fisher-proj.pdf){height=5.7cm}
 
-Aeral and lateral views of the projection direction, in blue, and the cut, in red.
+Aeral and lateral views of the projection direction, in blue, and the cut, in
+red.
 </div>
 
 Results obtained for the same sample in @fig:fisher_points are shown in
@@ -212,3 +214,78 @@ and $t_{\text{cut}}$ is 1.323 far from the origin of the axes. Hence, as can be
 seen, the vector $w$ turned out to be parallel to the line joining the means of
 the two classes (reminded to be $(0, 0)$ and $(4, 4)$) which means that the 
 total covariance matrix $S$ is isotropic, proportional to the unit matrix.
+
+
+## Perceptron
+
+In machine learning, the perceptron is an algorithm for supervised learning of
+linear binary classifiers.  
+Supervised learning is the machine learning task of inferring a function $f$
+that maps an input $x$ to an output $f(x)$ based on a set of training
+input-output pairs. Each example is a pair consisting of an input object and an
+output value. The inferred function can be used for mapping new examples. The
+algorithm will be generalized to correctly determine the class labels for unseen
+instances.
+
+The aim is to determine the threshold function $f(x)$ for the dot product
+between the (in this case 2D) vector point $x$ and the weight vector $w$:
+
+$$
+  f(x) = x \cdot w + b
+$$ {#eq:perc}
+
+where $b$ is called 'bias'. If $f(x) \geqslant 0$, than the point can be
+assigned to the class $C_1$, to $C_2$ otherwise.
+
+The training was performed as follow. The idea is that the function $f(x)$ must
+return 0 when the point $x$ belongs to the noise and 1 if it belongs to the 
+signal. Initial values were set as $w = (0,0)$ and $b = 0$. From  these, the
+perceptron starts to improve their estimations. The sample was passed point by
+point into a reiterative procedure a grand total of $N_c$ calls: each time, the
+projection $w \cdot x$ of the point was computed and then the variable $\Delta$ was defined as:
+
+$$
+  \Delta = r * (e - \theta (f(x))
+$$
+
+where:
+
+  - $r$ is the learning rate of the perceptron: it is between 0 and 1. The
+    larger $r$, the more volatile the weight changes. In the code, it was set
+    $r = 0.8$;
+  - $e$ is the expected value, namely 0 if $x$ is noise and 1 if it is signal;
+  - $\theta$ is the Heavyside theta function;
+  - $o$ is the observed value of $f(x)$ defined in @eq:perc.
+
+Then $b$ and $w$ must be updated as:
+
+$$
+  b \longrightarrow b + \Delta
+  \et
+  w \longrightarrow w + x \Delta
+$$
+
+<div id="fig:percep_proj">
+![View from above of the samples.](images/percep-plane.pdf){height=5.7cm}
+![Gaussian of the samples on the projection
+  line.](images/percep-proj.pdf){height=5.7cm}
+
+Aeral and lateral views of the projection direction, in blue, and the cut, in
+red.
+</div>
+
+It can be shown that this method converges to the coveted function.  
+As stated in the previous section, the weight vector must finally be normalzied.
+
+With $N_c = 5$, the values of $w$ and $t_{\text{cut}}$ level off up to the third
+digit. The following results were obtained:
+
+$$
+  w = (0.654, 0.756) \et t_{\text{cut}} = 1.213
+$$
+
+where, once again, $t_{\text{cut}}$ is computed from the origin of the axes. In
+this case, the projection line does not lies along the mains of the two
+samples. Plots in @fig:percep_proj.
+
+## Efficiency test