diff --git a/regression/exercises/exercises01.tex b/regression/exercises/exercises01.tex
index c6eadc1..043ee93 100644
--- a/regression/exercises/exercises01.tex
+++ b/regression/exercises/exercises01.tex
@@ -20,7 +20,7 @@
 \else
 \newcommand{\stitle}{}
 \fi
-\header{{\bfseries\large Exercise 11\stitle}}{{\bfseries\large Gradient descent}}{{\bfseries\large January 9th, 2018}}
+\header{{\bfseries\large Exercise 10\stitle}}{{\bfseries\large Gradient descent}}{{\bfseries\large December 17th, 2018}}
 \firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
   jan.grewe@uni-tuebingen.de}
 \runningfooter{}{\thepage}{}
@@ -59,12 +59,11 @@
 \begin{questions}
 
   \question Implement the gradient descent for finding the parameters
-  of a straigth line that we want to fit to the data in the file
-  \emph{lin\_regression.mat}.
+  of a straigth line \[ y = mx+b \] that we want to fit to the data in
+  the file \emph{lin\_regression.mat}.
 
   In the lecture we already prepared most of the necessary functions:
-  1. the error function (\code{meanSquareError()}), 2. the cost
-  function (\code{lsqError()}), and 3. the gradient
+  1. the cost function (\code{lsqError()}), and 2. the gradient
   (\code{lsqGradient()}). Read chapter 8 ``Optimization and gradient
   descent'' in the script, in particular section 8.4 and exercise 8.4!
 
@@ -72,8 +71,9 @@
   function is as follows:
   
   \begin{enumerate}
-  \item Start with some arbitrary parameter values $\vec p_0 = (m_0, b_0)$
-    for the slope and the intercept of the straight line.
+  \item Start with some arbitrary parameter values (intercept $b_0$
+    and slope $m_0$, $\vec p_0 = (b_0, m_0)$ for the slope and the
+    intercept of the straight line.
   \item \label{computegradient} Compute the gradient of the cost function
     at the current values of the parameters $\vec p_i$.
   \item If the magnitude (length) of the gradient is smaller than some